Recurrent Neural Networks (RNNs) are an important class of neural networks designed to retain and incorporate context into current decisions. RNNs are particularly well suited for machine learning problems in which context is important, such as speech recognition and language translation. This work presents RNNFast, a hardware accelerator for RNNs that leverages an emerging class of non-volatile memory

Intellectual property (IP) reuse is a well-known technique in chip design industry. But this technique also exposes a security vulnerability called IP stealing attack. Network-on-Chip (NoC) is an on-chip scalable communication medium and is used as an IP and sold by various vendors to be integrated in a Multiprocessor System-on-Chip (MPSoC). An attacker can launch IP stealing attack against NoC IP

Machine learning is at the heart of many services provided by data centers. To improve the performance of machine learning, several parameter (gradient) synchronization methods have been proposed in the literature. These synchronization algorithms have different communication characteristics and accordingly place different demands on the network architecture. However, traditional data-center networks

Networks of low-power Internet of Things do not have always access to enough computing power to support mainstream cryptographic schemes; such schemes also consume computing power that can be exposed to side channel attacks. This article describes a method, that we call “cryptography with analog scheme using memristors,” leveraging the physical properties of memristors, which are active elements suitable

Various applications, such as multimedia, machine learning, and signal processing, have a significant intrinsic error resilience. This makes them preferable for approximate computing as they have the ability to tolerate computations and data errors along with producing acceptable outputs. From the technology perspective, emerging technologies with inherent non-determinism and high failure rates are

Computing-in-memory (CIM) is proposed to alleviate the processor-memory data transfer bottleneck in traditional von Neumann architectures, and spintronics-based magnetic memory has demonstrated many facilitation in implementing CIM paradigm. Since hardware security has become one of the major concerns in circuit designs, this article, for the first time, investigates spin-based computing-in-memory

3D Network-on-Chip (3D NoC) enables design of high-performance and energy-efficient manycore computing platforms. Two of the commonly used vertical interconnection technologies are: through silicon via (TSV) and near-field inductive coupling (NFIC). Both TSV- and NFIC-based links introduce additional area overhead. One of the possible ways to reduce the area overhead is to design partially connected

Over the past two decades, globalized outsourcing in the semiconductor supply chain has lowered manufacturing costs and shortened the time-to-market for original equipment manufacturers (OEMs). However, such outsourcing has rendered the printed circuit boards (PCBs) vulnerable to malicious activities and alterations on a global scale. In this article, we take an in-depth look into one such attack,

We propose a novel architecture to enable low-power, complex on-node data processing, for the next generation of sensors for the internet of things (IoT), smartdust, or edge intelligence. Our architecture combines near-analog-memory-computing (NAM) and asynchronous-computing-with-streams (ACS), eliminating the need for ADCs. ACS enables ultra-low power, massive computational resources required to execute

Recent advances in deep neural network demand more than millions of parameters to handle and mandate the high-performance computing resources with improved efficiency. The cross-bar array architecture has been considered as one of the promising deep learning architectures that shows a significant computing gain over the conventional processors. To investigate the feasibility of the architecture, we

Neuromorphic computing based on spiking neural network (SNN) shows good energy-efficiency. However, it is inefficient for SNN to perform the convolution based on frame. It may contain a lot of redundant information in the frame. The output of Dynamic Vision Sensors (DVS) is a stream event based on Address Event Representation (AER). The asynchronous nature of AER events makes the event-based convolution

Energy harvesting is an attractive way to power future Internet of Things (IoT) devices since it can eliminate the need for battery or power cables. However, harvested energy is intrinsically unstable. While Field-programmable Gate Array (FPGAs) have been widely adopted in various embedded systems, it is hard to survive unstable power since all the memory components in FPGA are based on volatile Static

The advent of persistent memory (PM) into our everyday computing environment is now imminent. New programming models and algorithms based on these models are being developed for such systems. However, current models require programs to be rewritten with persistence related primitives such as clflush and clwb or at least recompiled so that persistent mechanisms can be automatically inserted. This is

Internet of Things (IoT) devices have stringent constraints on power and energy consumption. Adiabatic logic has been proposed as a novel computing platform to design energy-efficient IoT devices. Physically Unclonable Functions (PUFs) is a promising paradigm to solve security concerns such as Integrated Circuit (IC) piracy, IC counterfeiting, and the like. PUFs have shown great promise for generating

NISQ (Noisy, Intermediate-Scale Quantum) computing requires error mitigation to achieve meaningful computation. Our compilation tool development focuses on the fact that the error rates of individual qubits are not equal, with a goal of maximizing the success probability of real-world subroutines such as an adder circuit. We begin by establishing a metric for choosing among possible paths and circuit

Traditional computing hardware often encounters on-chip memory bottleneck on large-scale Convolution Neural Networks (CNN) applications. With its unique in-memory computing feature, resistive crossbar-based computing attracts researchers’ attention as a promising solution to the memory bottleneck issue in von Neumann architectures. However, the parasitic resistances in crossbar deviate its behavior

New and emerging types of Persistent Memory (PM) technologies boost the opportunity to improve the performance of storage systems. PM can unify the main memory and secondary storage by incorporating it into legacy computer systems through the memory bus. In recent years, innovative results have been presented that exploit the byte-addressability, low latency, and non-volatility of PM; these have included

In the past decade, complementary metal oxide semiconductor-memristor hybrid neuromorphic systems have gained importance owing to the advantages of memristors such as nano-scale size, non-volatility, and low-power operation. However, they are often accompanied by non-ideal properties that can impact the system’s performance. This article presents device-aware circuit design to mitigate such effects

Traditional memory technologies face severe challenges in meeting the ever-increasing power and memory bandwidth requirements for high-performance computing and big-data analyses. Several emerging memory technologies are promising as the replacements of SRAM or DRAM. Among them, STT-MRAM can be used to replace SRAM as the last-level cache (LLC). However, it suffers from high write energy and latency

With extreme miniaturization of traditional CMOS devices in deep sub-micron design levels, the delay of a circuit, as well as power dissipation and area are dominated by interconnections between logic blocks. Interconnect today is causing major problems such as delay, power dissipation, and so on. In an attempt to search for alternative materials, Graphene nanoribbons have been found to be potential

The design of near future cryptocircuits will require greater performance characteristics in order to be implemented in devices with very limited resources for secure applications. Considering the security against differential power side-channel attacks (DPA), explorations of different implementations of dual-precharge logic gates with advanced and emerging technologies, using nanometric FinFET and

Implantable systems for biomedical research and clinical care are now a flourishing field of activities in academia as well as industrial institutions. The broad field includes experimental explorations in electronics, mechanical, chemical, and biological components and systems, and the combination of all these. Today virtually all implants involve both electronic circuits and micro-electro-mechanical-systems

The wireless implantable/intracavity micromanometer (WIMM) system was designed to fulfill the unmet need for a chronic bladder pressure sensing device in urological fields such as urodynamics for diagnosis and neuromodulation for bladder control. Neuromodulation in particular would benefit from a wireless bladder pressure sensor which could provide real-time pressure feedback to an implanted stimulator