Malware detection at the hardware level has emerged recently as a promising solution
to improve the security of computing systems. Hardware-based malware detectors take
advantage of Machine Learning (ML) classifiers to detect pattern of malicious …

This paper presents DeepSecure, the an scalable and provably secure Deep Learning
(DL) framework that is built upon automated design, efficient logic synthesis, and
optimization methodologies. DeepSecure targets scenarios in which neither of the …

The Learning with Errors (LWE) problem is a novel foundation of a variety of cryptographic
applications, including quantumly-secure public-key encryption, digital signature,
and fully homomorphic encryption. In this work, we propose an approximate …

A convolutional neural network (CNN) model represents a crucial piece of intellectual
property in many applications. Revealing its structure or weights would leak confidential
information. In this paper we present novel reverse-engineering attacks on …

Journaling of ext4 file system employs two FLUSH commands to make their data durable,
even though the FLUSH is more expensive than the ordinary write operations. In this
paper, to halve the number of FLUSH commands, we propose an efficient FTL, called
…

Byte-addressable non-volatile memories can be used with DRAM to build a hybrid memory
system of volatile/non-volatile main memory (NVMM). NVMM file systems demand consistency
techniques such as logging and copy-on-write to guarantee data consistency in …

Copyback is an advanced command contributing to accelerating data migration in garbage
collection (GC). Unfortunately, detecting copyback feasibility (whether copyback can
be carried out with assurable reliability) against data corruption in the …

Garbage collection (GC) is one of main causes of the long-tail latency problem in
storage systems. Long-tail latency due to GC is more than 100 times greater than the
average latency at the 99^th percentile. Therefore, due to such a long tail latency, …

The emerging design requirements with the FinFET technology, along with traditional
geometrical constraints, make the FinFET-based analog placement even more challenging.
Previous works can handle only partial FinFET-induced design constraints because …

Modern analog placement techniques require consideration of current path and symmetry
constraints. The symmetry pairs can be efficiently packed using the symmetry island
configurations, but not all these configurations result in minimum gate …

In this paper, a novel multi-objective Bayesian optimization method is proposed for
the sizing of analog/RF circuits. The proposed approach follows the framework of Bayesian
optimization to balance the exploitation and exploration. Gaussian processes (…

Process Variation (PV) may cause accuracy loss of the analog neural network (ANN)
processors, and make it hard to be scaled down, as well as feasibility degrading.
This paper first analyses the impact of PV on the performance of ANN chips. Then proposes
…

Stochastic computing (SC) is a promising technique with advantages such as low-cost,
low-power, and error-resilience. However so far SC-based CNN (convolutional neural
network) accelerators have been kept to relatively small CNNs only, primarily due
to …

Deep Neural Networks (DNNs) have demonstrated their utility across a wide range of
input data types, usable across diverse computing substrates, from edge devices to
datacenters. This broad utility has resulted in myriad hardware accelerator …

This paper presents an instruction and Fabric Programmable Neuron Array (iFPNA) architecture, its 28nm CMOS chip prototype, and a compiler for
the acceleration of a variety of deep learning neural networks (DNNs) including convolutional
neural networks (…

Deep convolutional neural networks (DCNNs) have been widely used in various AI applications.
Inception and Residual are two promising structures adopted in many important modern
DCNN models, including AlphaGo Zero’s model. These structures allow …

As the use of deep neural networks continues to grow, so does the fraction of compute
cycles devoted to their execution. This has led the CAD and architecture communities
to devote considerable attention to building DNN hardware. Despite these efforts,
…

As one of most fascinating machine learning techniques, deep neural network (DNN)
has demonstrated excellent performance in various intelligent tasks such as image
classification. DNN achieves such performance, to a large extent, by performing expensive
…

Hardware accelerators are being increasingly deployed to boost the performance and
energy efficiency of deep neural network (DNN) inference. In this paper we propose
Thundervolt, a new framework that enables aggressive voltage underscaling of high-…

Loom (LM), a hardware inference accelerator for Convolutional Neural Networks (CNNs) is presented.
In LM every bit of data precision that can be saved translates to proportional performance
gains. For both weights and activations LM exploits profile-…

Recent advances in deep neural networks (DNNs) have shown Binary Neural Networks (BNNs)
are able to provide a reasonable accuracy on various image datasets with a significant
reduction in computation and memory cost. In this paper, we explore two BNNs: …

Vector-matrix multiplication (VMM) is a core operation in many signal and data processing
algorithms. Previous work showed that analog multipliers based on nonvolatile memories
have superior energy efficiency as compared to digital counterparts at low-…

Through-silicon vias (TSVs) in 3D ICs show a significant power consumption, which
can be reduced using coding techniques. This work presents an approach which reduces
the TSV power consumption by a signal-aware bit assignment which includes inversions
…

The first mainstream products in 3D IC design are memory devices where multiple memory
tiers are horizontally integrated to offer manifold improvements compared with their
2D counterparts. Unfortunately, none of these existing 3D memory cubes are ready …

In this paper, we develop a general pessimism reduction framework for design optimization
of timing closure. Although the modified graph based timing analysis (mGBA) slack
model can be readily formulated into a quadratic programming problem with …

In digital circuit designs, sequential components such as flip-flops are used to synchronize
signal propagations. Logic computations are aligned at and thus isolated by flip-flop
stages. Although this fully synchronous style can reduce design efforts …

Low power system-on-chips (SoCs) are now at the heart of Internet-of-Things (IoT)
devices, which are well known for their bursty workloads and limited energy storage
— usually in the form of tiny batteries. To ensure battery lifetime, DVFS has become
…

Faithful system-level modeling is vital to design and technology pathfinding, and
requires accurate representation of interconnects. In this study, Rent’s rule is modernized
to cater to advanced technology and design, and applied to derive a priori …

The complexity of safety-critical E/E-systems within the automotive domain are continuously
increasing. At the same time, functional safety standards such as the ISO 26262 prescribe
analysis methods like the Fault Tree Analysis (FTA) and Failure Mode …

We present a simulation-based approach for generating barrier certificate functions
for safety verification of cyber-physical systems (CPS) that contain neural network-based
controllers. A linear programming solver is utilized to find a candidate …

Advanced driver-assistance systems (ADAS), autonomous driving, and connectivity have
enabled a range of new features, but also made automotive design more complex than
ever. Formal verification can be applied to establish functional correctness, but
its …

The emergence of IoT poses new challenges towards solutions for authenticating numerous
very heterogeneous IoT devices to their respective trust domains. Using passwords
or pre-defined keys have drawbacks that limit their use in IoT scenarios. Recent …

This paper presents MAXelerator, the first hardware accelerator for privacy-preserving
machine learning (ML) on cloud servers. Cloud-based ML is being increasingly employed
in various data sensitive scenarios. While it enhances both efficiency and …

Large OS kernels always suffer from attacks due to their numerous inherent vulnerabilities.
To protect the kernel, hypervisors have been employed by many security solutions.
However, relying on a hypervisor has a detrimental impact on the system …

Data stored in an off-chip memory, such as DRAM or non-volatile main memory, can potentially
be extracted or tampered by an attacker with physical access to a device. Protecting
such attacks requires storing message authentication codes and counters – …

To reduce time and effort in IC implementation, fundamental challenges must be solved.
First, the need for (expensive) humans must be removed wherever possible. Humans are
skilled at predicting downstream flow failures, evaluating key early decisions …

The exponential growth in PVT corners due to Moore’s law scaling, and the increasing
demand for consumer applications and longer battery life in mobile devices, has ushered
in significant cost and power-related challenges for designing and productizing …

Deep Neural Networks (DNNs) represent the state-of-the-art in many Artificial Intelligence
(AI) tasks involving images, videos, text, and natural language. Their ubiquitous
adoption is limited by the high computation and storage requirements of DNNs, …

ReRAM-based systems are attractive implementation alternatives for neuromorphic computing
because of their high speed and low design cost. In this work, we investigate the
impact of temperature on the ReRAM-based neuromorphic architectures and show how …

Despite the significant promise that circuit-level timing speculation has for enabling
operation in marginal conditions, overheads associated with recovery prove to be a
serious drawback. We show that fine-grained clock adjustment guided by the compiler
…

Energy-efficient microprocessors are essential for a wide range of applications. While
near-threshold computing is a promising technique to improve energy efficiency, optimal
supply demands from logic core and on-chip memory are conflicting. In this …

Ultra-low-power (ULP) chipsets are in higher demand than ever due to the proliferation
of ULP embedded systems to support growing applications like the Internet of Things
(IoT), wearables and sensor networks. Since ULP systems are also cost constrained,
…

Voltage stacking (VS) fundamentally improves power delivery efficiency (PDE) by series-stacking
multiple voltage domains to eliminate explicit step-down voltage conversion and reduce
energy loss along the power delivery path. However, it suffers from …

Rectilinear Steiner arborescences under linear delay constraints play an important
role for buffering. We present exact algorithms for either minimizing the total length
subject to delay constraints, or minimizing the total length plus the (weighted) …

As Engineering Change Order (ECO) has attracted substantial attention in modern VLSI
design, the open net problem, which aims at constructing a shortest obstacle-avoiding
path to reconnect the net shapes in an open net, becomes more critical in the ECO
…

At the end of digital integrated circuit (IC) design flow, some nets may still be
left open due to engineering change order (ECO). Resolving these opens could be quite
challenging for some huge nets such as power ground nets because of a large number
of …

Slew fixing, which ensures correct signal propagation, is essential during timing
closure of IC design flow. Conventionally, gate sizing, Vt swapping, or buffer insertion
is adopted to locally fix the slew violation on a single gate. Nevertheless, when
…

Detecting and preventing routing violations has become a critical issue in physical
design, especially in the early stages. Lack of correlation between global and detailed
routing congestion estimations and the long runtime required to frequently …

As integrated circuit technology nodes continue to shrink, dense via distribution
becomes a severe challenge, requiring multiple masks to avoid spacing violations in
via layers. Meanwhile, the directed self-assembly (DSA) technique shows a great promise
…

Design flows are the explicit combinations of design transformations, primarily involved
in synthesis, placement and routing processes, to accomplish the design of Integrated
Circuits (ICs) and System-on-Chip (SoC). Mostly, the flows are developed based …

Engineering Change Orders (ECO) modify a synthesized netlist after its specification
has changed. ECO is divided into two major tasks: finding target signals whose functions
should be updated and synthesizing the patch that produces the desired change. …

A representation of a Boolean function is canonical if, given a variable order, only one instance of the representation is possible for
the function. A computation is canonical if the result depends only on the Boolean function and a variable order, and …

SAT based exact synthesis is a powerful technique, with applications in logic optimization,
technology mapping, and synthesis for emerging technologies. However, its runtime
behavior can be unpredictable and slow. In this paper, we propose to add a new …

Approximate computing is an emerging energy-efficient paradigm for error-resilient
applications. Approximate logic synthesis (ALS) is an important field of it. To improve
the existing ALS flows, one key issue is to derive a more accurate and efficient …

Approximate computing is an emerging paradigm where design accuracy can be traded
off for benefits in design metrics such as design area, power consumption or circuit
complexity. In this work, we present a novel paradigm to synthesize approximate …

Non-volatile memory express (NVMe) over peripheral component interconnect express
(PCIe) has been adopted in the storage system to provide low latency and high throughput.
NVMe allows a host system to reduce latency because it offers a high parallel …

Due to the cost consideration for data storage, high-areal-density shingled-magnetic-recording
(SMR) drives and data deduplication techniques are getting popular in many data storage
services for the improvement of profit per storage unit. However, …

Resistive Memory (ReRAM) is an emerging non-volatile memory technology that has many
advantages over conventional DRAM. ReRAM crossbar has the smallest 4F² planar cell size and thus is widely adopted for constructing dense memory with large
capacity. …

Next Generation Sequencing (NGS) technology has become an indispensable tool for studying
genomics, resulting in an exponentially growth of biological data. Booming data volume
demands significant computational resources and creates challenges for ‘…

Modern high-level languages bring compelling productivity benefits to hardware design
and verification. For example, hardware generation and simulation frameworks (HGSFs)
use a single “host” language for parameterization, static elaboration, test bench
…

Designers translate DSP algorithms into application-specific hardware via primitives
composed in various ways for different architectural realizations. Despite sharing
underlying algorithms and hardware constructs, designs are often difficult to reuse,
…

Reliability is a major concern in chip multi-processors (CMPs) due to shrinking technology
and low operating voltages. Today’s processors designed at sub-10nm technology nodes
have high device densities and fast switching frequencies that cause …

Circuit aging has become a dire design concern and hence it is considered a primary
design constraint. Current practice to cope with this problem is to apply (too) conservative
means.

In contrast, we introduce a far less restrictive approach by …

Battery-backed security enclosures that are permanently monitored for penetration
and tampering are common solutions for providing physical integrity to multi-chip
embedded systems. This paper presents a well-tailored measurement system for a …

Physically Unclonable Functions (PUFs) are still considered promising technology as
building blocks in cryptographic protocols. While most PUFs require dedicated circuitry,
recent research leverages DRAM hardware for PUFs due to its intrinsic properties …

Physically unclonable functions (PUFs) have proved to be an effective measure for
secure device authentication and key generation. We propose a novel PUF design, named
CamPUF, based on commercial off-the-shelf CMOS image sensors, which are ubiquitously
…

Digital microfluidic biochips (DMFBs)—an emerging technology that implements bioassays
through manipulation of discrete fluid droplets—are vulnerable to actuation tampering
attacks, where a malicious adversary modifies control signals for the …

Run-time resource management of heterogeneous multi-core systems is challenging due
to i) dynamic workloads, that often result in ii) conflicting knob actuation decisions,
which potentially iii) compromise on performance for thermal safety. We present a
…

A many-core processor can execute hundreds of multi-threaded tasks in parallel on
its 100s – 1000s of processing cores. When deployed in a Quality of Service (QoS)-based
system, the many-core must execute a task at a target QoS. The amount of processing
…

Approximate computing has recently reemerged as a design solution for additional performance
and energy improvements at the cost of output quality. In this paper, we propose using
a tree-based classification algorithm as an approximation tool for …

Conventional hard real-time scheduling is often overly pessimistic due to the worst
case execution time estimation. The pessimism can be mitigated by exploiting imprecise
computing in applications where occasional small errors are acceptable. This …

A high-productivity digital VLSI flow for designing complex SoCs is presented. The
flow includes high-level synthesis tools, an object-oriented library of synthesizable
SystemC and C++ components, and a modular VLSI physical design approach based on …

We propose a Standard Template Library (STL) for synthesizeable SystemVerilog that
sharply reduces the time required to design digital circuits. We overview the principles
that underly the design of the open-source BaseJump STL, including light-weight …

The energy efficiency demands of future abundant-data applications, e.g., those which
use inference-based techniques to classify large amounts of data, exceed the capabilities
of digital systems today. Field-effect transistors (FETs) built using …

As VLSI technology scales to deep sub-micron, optical interconnect becomes an attractive
alternative for on-chip communication. The traditional optical routing works mainly
optimize the path loss, and few works explicitly exploit the optical-electrical …

Phase Transition Material (PTM) assisted novel soft switching transistor architecture
named “Soft-FET” is proposed for supply voltage droop mitigation. By utilizing the
abrupt phase transition mechanism in PTMs, the proposed Soft-FET achieves soft …

This paper discusses energy-efficient acoustic feature-scoring using transistors with
Gaussian-shaped Ids-Vgs. Acoustic feature-scoring is a critical step in speech recognition tasks such as
speaker recognition. Suited to the transistor, we discuss a …

X-values in test output responses corrupt an output response compaction and can cause
a fault coverage loss. X-Masking and X-Canceling MISR methods have been suggested to eliminate X-values, however, there are control data volume and test time overhead …

With shrinking structure sizes, soft-error mitigation has become a major challenge
in the design and certification of safety-critical embedded systems. Their robustness
is quantified by extensive fault-injection campaigns, which on hardware level can
…

Continuous pursuit of higher performance and energy efficiency has led to heterogeneous
SoC that contains multiple dedicated hardware accelerators. These accelerators exploit
the inherent parallelism of tasks and are often tolerant to inaccuracies in …

Photonic networks-on-chip (PNoCs) enable high bandwidth on-chip data transfers by
using photonic waveguides capable of dense-wave-length-division-multiplexing (DWDM)
for signal traversal and microring resonators (MRs) for signal modulation. A Hardware
…

Network on Chips (NoCs) are the interconnect fabric of choice for multicore processors
due to their superiority over traditional buses and crossbars in terms of scalability.
While NoC’s offer several advantages, they still suffer from high static and …

Parallel applications are essential for efficiently using the computational power
of a Multiprocessor System-on-Chip (MPSoC). Unfortunately, these applications do not
scale effortlessly with the number of cores because of synchronization operations
that …

In order to fully exploit GPGPU’s parallel processing power, on-chip interconnects
need to provide bandwidth efficient data communication. GPGPUs exhibit a many-to-few-to-many
traffic pattern which makes the memory controller connected routers the …

Whereas emerging non-volatile memories (NVMs) are low power, dense, scalable alternatives
to DRAM, the high latency and low endurance of these NVMs limit the feasibility of
NVM-only memory systems. Smart hybrid memories (SHMs) that integrate NVM, DRAM, …

Modern computing systems that integrate emerging non-volatile memories (NVMs) are
vulnerable to classical security threats to data confidentiality (e.g., stolen DIMM
and bus snooping attacks) as well as new security threats to system availability (e.g.,
…

CASTLE is a Compression-based main memory Architecture realizing a read-decrypt-free
(i.e., write-only) Secure solution for low laTency, Low Energy, high endurance non-volatile
memories (NVMs). CASTLE integrates pattern-based data compression and …

While the Internet of Things (IoT) keeps advancing, its full adoption is continually
blocked by power delivery problems. One promising solution is Non-Volatile (NV) processors,
which harvest energy for themselves and employ a NV memory hierarchy. This …

Modern automotive systems and IoT devices are designed through a highly complex, globalized,
and potentially untrustworthy supply chain. Each player in this supply chain may (1)
introduce sensitive information and data (collectively termed “assets”) …

Remote attestation (RA) is a means of malware detection, typically realized as an
interaction between a trusted verifier and a potentially compromised remote device
(prover). RA is especially relevant for low-end embedded devices that are incapable
of …

Formal security verification of firmware interacting with hardware in modern Systems-on-Chip
(SoCs) is a critical research problem. This faces the following challenges: (1) design
complexity and heterogeneity, (2) semantics gaps between software and …

We present a method for selecting trace messages for post-silicon validation of Systems-on-a-Chips
(SoCs) with diverse usage scenarios. We model specifications of interacting flows
in typical applications. Our method optimizes trace buffer utilization …

Due to the increasing complexity of System-on-Chip (SoC) design, how to ensure that
silicon implementations conform to their high-level specifications is becoming a major
challenge. To address this problem, we propose a novel specification-driven …

In the realm of Multi-Processors System-on-Chip (MPSoC’s), the Network-on-Chip (NoC)
connecting all system components plays a crucial role in the overall correctness and
performance of the system. Recent papers have proposed several ring based NoC …

With increasing design complexity and robustness requirement, analog and mixed-signal
(AMS) verification manifests itself as a key bottleneck. While formal methods and
machine learning have been proposed for AMS verification, these two techniques suffer
…

The increasing system complexity makes engineering change order (ECO) mostly inevitable
and a common practice in integrated circuit design. Despite extensive research being
made, prior methods are not effectively applicable to instances where …

Multicores are becoming ubiquitous in automotive. Yet, the expected benefits on integration
are challenged by multicore contention concerns on timing V&V. Worst-case execution
time (WCET) estimates are required as early as possible in the software …

Embedded computers control an increasing number of systems directly interacting with
humans, while also manage more and more personal or sensitive information. As a result,
both safety and security are becoming ubiquitous requirements in embedded …

We present Non-interference Analysis as a model-based method to automatically reveal,
track and analyze end-to-end timing dependencies as part of a cross-layer dependency
analysis in complex systems. Based on revealed timing dependencies of functional …

Modern automotive systems require increased performance to implement Advanced Driving
Assistance Systems (ADAS). GPU-powered platforms are promising candidates for such
computational tasks, however current low-level programming models challenge the …

Future automotive software needs to deal with an increasing level of dynamicity, reasoned
by the wish for connected driving, software updates, and dynamic feature activation.
Such functionalities cannot be properly realized with today’s classic AUTOSAR …

In modern vehicles, high communication complexity requires cost-effective integration
tests such as data-driven system verification with in-vehicle network traces. With
the growing amount of traces, distributable Big Data solutions for analyses become
…

Although ReRAM-based convolutional neural network (CNN) accelerators have been widely
studied, state-of-the-art solutions suffer from either incapability of training (e.g.,
ISSAC [1]) or inefficiency of inference (e.g., PipeLayer [2]) due to the …

Deep Neural Networks (DNNs) have gained immense success in cognitive applications
and greatly pushed today’s artificial intelligence forward. The biggest challenge
in executing DNNs is their extremely data-extensive computations. The computing …

In this paper, an energy-efficient and high-speed comparator-based processing-in-memory
accelerator (CMP-PIM) is proposed to efficiently execute a novel hardware-oriented
comparator-based deep neural network called CMPNET. Inspired by local binary …

The sparsity in the deep neural networks can be leveraged by methods such as pruning
and compression to help the efficient deployment of large-scale deep neural networks
onto hardware platforms, such as GPU or FPGA, for better performance and power …

Deeper and larger Neural Networks (NNs) have made breakthroughs in many fields. While
conventional CMOS-based computing platforms are hard to achieve higher energy efficiency.
RRAM-based systems provide a promising solution to build efficient Training-…

Brain-inspired Hyperdimensional (HD) computing emulates cognition tasks by computing
with hypervectors rather than traditional numerical values. In HD, an encoder maps
inputs to high dimensional vectors (hypervectors) and combines them to generate a
…

As a critical operation in robotics, motion planning consumes lots of time and energy,
especially in a dynamic environment. Through approaches based on general-purpose processors,
it is hard to get a valid planning in real time. We present an …

We propose a technique to reduce compulsory misses of packet processing cache (PPC),
which largely affects both throughput and energy of core routers. Rather than prefetching
data, our technique called response prediction cache (RPC) speculatively …

Computing with high-dimensional (HD) vectors, also referred to as hypervectors, is a brain-inspired alternative to computing with scalars. Key properties of HD
computing include a well-defined set of arithmetic operations on hypervectors, generality,
…

Accelerator-rich architectures employ IOMMUs to support unified virtual address, but
researches show that they fail to meet the performance and energy requirements of
accelerators. Instead of optimizing the speed/energy of IOMMU address translation,
…

In modern heterogeneous MPSoCs, the management of shared memory resources is crucial
in delivering end-to-end QoS. Previous frameworks have either focused on singular
QoS targets or the allocation of partitionable resources among CPU applications at
…

The demand for multitasking GPUs increases whenever the GPU may be shared by multiple
applications, either spatially or temporally. This requires that GPUs can be preempted
and switch context to a new application while already executing one. Unlike CPUs,…

Address translation is increasingly a performance bottleneck in flash-based SSDs (solid
state drives). We propose a hardware-accelerated flash map management unit called
FMMU to speed up the address translation. The FMMU operates in a non-blocking …

Due to the decreasing endurance of flash chips, the lifetime of flash drives has become
a critical issue. To resolve this issue, various techniques such as wear-leveling
and error correction code have been proposed to reduce the bit error rates of flash
…

With the low encoding/decoding complexity and the high error correction capability,
polar code with the support of list-decoding and cyclic redundancy check can outperform
LDPC code in the area of data communication. Thus, it also draws a lot of …

To store the desired data on MLC and TLC flash memories, the conventional programming
strategies need to divide a fixed range of threshold voltage (V_t) window into several parts. The narrowly partitioned V_t window in turn limits the design of …

Modern embedded computing devices are vulnerable against malware and software piracy
due to insufficient security scrutiny and the complications of continuous patching.
To detect malicious activity as well as protecting the integrity of executable …

Side-channel attacks are performed on increasingly complex targets, starting to threaten
superscalar CPUs supporting a complete operating system. The difficulty of both assessing
the vulnerability of a device to them, and validating the effectiveness of …

In this work, for the first time, we investigate Electro-Magnetic (EM) attacks on
GPU-based AES implementation. In detail, we first sample EM traces using a delicate
trigger; then, we build a heuristic leakage model and a novel leakage model to exploit
…

Conventional attacks against existing logic obfuscation techniques rely on the presence
of an activated hardware for analysis. In reality, obtaining such activated chips may not always be practical,
especially if the on-chip test structures are …

Autonomous vehicles in embedded real-time systems increase critical-software size
and complexity whose performance needs are covered with high-performance hardware
features like caches, which however hampers obtaining WCET estimates that hold valid
for …

Heterogeneous multicore processors have become popular computing engines for modern
embedded real-time systems recently. However, there is rather limited research on
the scheduling of real-time tasks running on heterogeneous multicore systems with
…

Hardware platforms are evolving towards parallel and heterogeneous architectures to
overcome the increasing necessity of more performance in the real-time domain. Parallel
programming models are fundamental to exploit the performance capabilities of …

The increasingly high display resolution of mobile devices imposes a further burden
on energy consumption. Existing schemes manage either OLED or GPU power to save energy.
This paper presents the design, algorithm, and implementation of a co-managing …

Coarse-grained reconfigurable array (CGRA) is a promising solution that can accelerate
even non-parallel loops. Acceleration achieved through CGRAs critically depends on
the goodness of mapping (of loop operations onto the PEs of CGRA), and in …

Coarse-grained reconfigurable architectures (CGRAs) have gained traction as a potential
solution to implement accelerators for compute-intensive kernels, particularly in
domains requiring hardware programmability. Architecture and CAD for CGRAs are …

Coarse-Grained Reconfigurable Arrays (CGRAs) provide high performance, energy-efficient
execution of the innermost loops of an application. Most real-world applications,
however, comprise of deeply-nested loops with complex and often irregular control
…

With the trend toward specialization, an efficient memory-path design is vital to
capitalize customization in data-path. A monolithic memory hierarchy is often highly
inefficient for irregular applications, traditionally targeted for CPUs. New …

Mask optimization has been a critical problem in the VLSI design flow due to the mismatch
between the lithography system and the continuously shrinking feature sizes. Optical
proximity correction (OPC) is one of the prevailing resolution enhancement …

With increasing dimension of variation space and computational intensive circuit simulation,
accurate and fast yield estimation of realistic SRAM chip remains a significant and
complicated challenge. In this paper, du Experiment results show that the …

In this paper, we study the main interconnect aging processes: electromigration, thermomigration
and stress migration and propose comprehensive yet compact models for transient and
steady states based on hydrostatic stress evolution. Our model can be …

Performance failure has become a growing concern for the robustness and reliability
of memory circuits. It is challenging to accurately estimate the extremely small failure
probability when failed samples are distributed in multiple disjoint failure …

FPGAs have been an efficient accelerator for CNN inference due to its high performance,
flexibility, and energy-efficiency. To improve the performance of CNNs on FPGAs, fast
algorithms and sparse methods emerge as the most attractive alternatives, which …

The implementation of Convolutional Neural Networks on edge Internet of Things (IoT)
devices is a significant programming challenge, due to the limited computational resources
and the real-time requirements of modern applications. This work focuses on …

While deep convolutional neural networks (CNNs) have emerged as the driving force
of a wide range of domains, their computationally and memory intensive natures hinder
the further deployment in mobile and embedded applications. Recently, CNNs with low-…

Binarized neural network (BNN) is one of the most promising solution for low-cost
convolutional neural network acceleration. Since BNN is based on binarized bit-level
operations, there exist great opportunities to reduce power-hungry data transfers
and …

SoCFPGAs or FPGAs integrated on the same die with chip multi processors have made
it to the market in the past years. In this article we analyse various security loopholes,
existing precautions and countermeasures in these architectures. We consider …

Split manufacturing (SM) seeks to protect against piracy of intellectual property
(IP) in chip designs. Here we propose a scheme to manipulate both placement and routing
in an intertwined manner, thereby increasing the resilience of SM layouts. Key …

This work is the first to analyze the security of split manufacturing using machine
learning, based on data collected from layouts provided by industry, with 8 routing
metal layers, and significant variation in wire size and routing congestion across
…

Electromagnetic fault injection (EMFI) is an efficient class of physical attacks that
can compromise the immunity of secure cryptographic algorithms. Despite successful
EMFI attacks, the effects of electromagnetic injection (EM) on a processor are not
…

According to the U.S. Food and Drug Administration (FDA) medical device recall database,
medical device recalls are at an all-time high. One of the major causes of the recalls
is due to implicit assumptions of which either the medical device operating …

Formal specifications on temporal behavior of Cyber-Physical Systems (CPS) is essential
for verification of performance and safety. Existing solutions for verifying the satisfaction
of temporal constraints on a CPS are compute and resource intensive …

Energy-constrained Systems-on-Chips (SoC) are becoming major components of many emerging
applications, especially in the Internet of Things (IoT) domain. Although the best
energy efficiency is achieved when the SoC operates in the near-threshold region,
…

Limited processing power and memory prevent realization of state of the art algorithms
on the edge level. Offloading computations to the cloud comes with tradeoffs as compression
techniques employed to conserve transmission bandwidth and energy …

Deep learning algorithms are achieving increasingly higher prediction accuracy on
many machine learning tasks. However, applying brute-force programming to data demands
a huge amount of machine power to perform training and inference, and a huge amount
…

Deep Learning is arguably the most rapidly evolving research area in recent years.
As a result it is not surprising that the design of state-of-the-art deep neural net
models proceeds without much consideration of the latest hardware targets, and the
…

Global placement dominates the circuit placement process in its solution quality and
efficiency. With increasing design complexity and various design constraints, it is
desirable to develop an efficient, high-quality global placement algorithm for …

Placement is one of the most critical stages in the physical synthesis flow. Circuits
with increasing numbers of cells of multi-row height have brought challenges to traditional
placers on efficiency and effectiveness. Furthermore, constraints on fence …

Optical networks-on-chips (ONoCs) have become a promising solution for the on-chip
communication of multi-and many-core systems to provide superior communication bandwidths,
efficiency in power consumption, and latency performance compared to electronic …

In recent years, spectral graph sparsification techniques that can compute ultra-sparse
graph proxies have been extensively studied for accelerating various numerical and
graph-related applications. Prior nearly-linear-time spectral sparsification …

Big data analytics using the JVM-based MapReduce framework has become a popular approach
to address the explosive growth of data sizes. Adopting FPGAs in datacenters as accelerators
to improve performance and energy efficiency also attracts increasing …

CPU-FPGA heterogeneous architectures feature flexible acceleration of many workloads
to advance computational capabilities and energy efficiency in today’s datacenters.
This advantage, however, is often overshadowed by the poor programmability of FPGAs.
…

Intellectual Property (IP) theft costs semiconductor design companies billions of
dollars every year. Unauthorized IP copies start from reverse engineering the given
chip. Existing techniques to protect against IP theft aim to hide the IC’s …

Irregular memory access pattern in non-stencil kernel computing renders the well-known
hyperplane- [1], lattice- [2], or tessellation-based [3] HLS techniques ineffective.
We develop an elegant yet effective technique that synthesizes memory-optimal …

The main focus of the existing approximate arithmetic circuits has been on ASIC-based
designs. However, due to the architectural differences between ASICs and FPGAs, comparable
performance gains cannot be achieved for FPGA-based systems by using the …

Stochastic computing (SC) is a promising computing paradigm for applications with
low precision requirement, stringent cost and power restriction. One known problem
with SC, however, is the low accuracy especially with multiplication. In this paper
we …

The architectural differences between ASICs and FPGAs limit the effective performance
gains achievable by the application of ASIC-based approximation principles for FPGA-based
reconfigurable computing systems. This paper presents a novel approximate …

Approximate functional unit designs have the potential to reduce power consumption
significantly compared to their precise counterparts; however, few works have investigated
composing them to build generic accelerators. In this work, we do a design-…

Approximate computing aims to trade accuracy for energy efficiency. Various approximate
methods have been proposed in the literature that demonstrate the effectiveness of
relaxing accuracy requirements in a specific unit. This provides a basis for …

In this paper, we propose PIMA-Logic, as a novel <u>P</u>rocessing-<u>i</u>n-<u>M</u>emory
<u>A</u>rchitecture for highly flexible and efficient <u>Logic</u> computation. Insteadof
integrating complex logic units in cost-sensitive memory, PIMA-Logic …

Microfluidic large-scale integration (mLSI) is a promising platform for high-throughput
biological applications. Design automation for mLSI has made much progress in recent
years. Columba and its succeeding work Columba 2.0 proposed a mathematical …

Flow-based microfluidic biochips are gaining traction in the microfluidics community
since they enable efficient and low-cost biochemical experiments. These highly integrated
lab-on-a-chip systems, however, suffer from manufacturing defects, which cause …

A liquid state machine (LSM) is a powerful recurrent spiking neural network shown
to be effective in various learning tasks including speech recognition. In this work,
we investigate design and architectural co-optimization to further improve the area-…

The reservoir computing, an emerging computing paradigm, has proven its benefit to
multifarious applications. In this work, we successfully designed and fabricated an
analog delayed feedback reservoir (DFR) chip. Measurement results demonstrate its
rich …

The recent proposed nanoscale Mott memristor features negative differential resistance
and chaotic dynamics. This work proposes a novel neuromorphic computing system that
utilizes Mott memristors to simplify peripheral circuitry. According to the …

Modern Convolutional Neural Networks (CNNs) are computation and memory intensive.
Thus it is crucial to develop hardware accelerators to achieve high performance as
well as power/energy-efficiency on resource limited embedded systems. DRAM-based CNN
…

One of the biggest performance bottlenecks of today’s neural network (NN) accelerators
is off-chip memory accesses [11]. In this paper, we propose a method to use multi-level,
embedded nonvolatile memory (eNVM) to eliminate all off-chip weight accesses. …

DRAM memory access is a critical performance bottleneck. To access one cache block,
an entire row needs to be sensed and amplified, data restored into the bitcells and
the bitlines precharged, incurring high latency. Isolating the bitlines and sense
…

A DRAM chip requires periodic refresh operations to prevent data loss due to charge
leakage in DRAM cells. Refresh operations incur significant performance overhead as
a DRAM bank/rank becomes unavailable to service access requests while being …

Due to the fast access performance, byte-addressability, and non-volatility of non-volatile
random access memory (NVRAM), NVRAM has emerged as a popular candidate for the design
of memory/storage systems on mobile computing systems. For example, the …

Today’s mobile platforms have grown in sophistication to run a wide variety of frame-based
applications. To deliver better QoS and energy efficiency, these applications utilize
multi-flow execution, which exploits hardware-level parallelism across …

Today’s embedded systems operate under increasingly dynamic conditions. First, computational
workloads can be either fluctuating or adjustable. Moreover, as many devices are battery-powered,
it is common to have runtime power management technique, which …

Determining feasible application mappings for Design Space Exploration (DSE) and run-time
embedding is a challenge for modern many-core systems. The underlying NP-complete
system-synthesis problem faces tremendously complex problem instances due to the …

Existent GPU simulators are too slow to use for neural networks implemented in GPUs.
For fast performance estimation, we propose a novel hybrid method of analytical performance
modeling and sampled simulation of GPUs. By taking full advantage of …

Dynamic resource management techniques rely on power consumption and performance models
to optimize the operating frequency and utilization of processing elements, such as
CPU and GPU. Despite the importance of these decisions, many existing approaches …

To take advantage of the performance enhancements provided by multicore processors,
new instruction set architectures (ISAs) and parallel programming libraries have been
investigated across multiple industrial segments. This paper investigates the …

We address the design space exploration of wireless networks to jointly select topology
and component sizing. We formulate the exploration problem as an optimized mapping
problem, where network elements are associated with components from pre-defined …