scholarly journals Modeling and Simulation of Asynchrony in Neuromorphic Computing

2019 ◽  
Vol 8 (9) ◽  
pp. 676-685
Keyword(s):  
Neural Network ◽  
Human Brain ◽  
Hardware Implementation ◽  
Electronic System ◽  
Clock Generator ◽  
Asynchronous Circuit ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Von Neumann Architecture ◽  
Computational Element

Neuromorphic computing is a non-von Neumann architecture which is also referred to as artificial neural network and that allows electronic system to function in the same manner as that of the human brain. In this paper we have developed neural core architecture analogous to that of the human brain. Each neural core has its own computational element neuron, memory to store information and local clock generator for synchronous functioning of neuron along with asynchronous input-output port and its port controller. The neuron model used here is a tailor-made of IBM TrueNorth’s neuron block. Our design methodology includes both synchronous and asynchronous circuit in order to build an event-driven neural network core. We have first simulated our design using Neuroph studio in order to calculate the weights and bias value and then used these weights for hardware implementation. With that we have successfully demonstrated the working of neural core using XOR application. It was designed in VHDL language and simulated in Xilinx ISE software.

scholarly journals Memristive and CMOS Devices for Neuromorphic Computing

Materials ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 166 ◽  
Author(s):  
Valerio Milo ◽  
Gerardo Malavena ◽  
Christian Monzio Compagnoni ◽  
Daniele Ielmini
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Low Power ◽  
Complementary Metal Oxide Semiconductor ◽  
Oxide Semiconductor ◽  
Neuromorphic Computing ◽  
Ultra Low Power ◽  
Von Neumann ◽  
Novel Device ◽  
Von Neumann Architecture

Neuromorphic computing has emerged as one of the most promising paradigms to overcome the limitations of von Neumann architecture of conventional digital processors. The aim of neuromorphic computing is to faithfully reproduce the computing processes in the human brain, thus paralleling its outstanding energy efficiency and compactness. Toward this goal, however, some major challenges have to be faced. Since the brain processes information by high-density neural networks with ultra-low power consumption, novel device concepts combining high scalability, low-power operation, and advanced computing functionality must be developed. This work provides an overview of the most promising device concepts in neuromorphic computing including complementary metal-oxide semiconductor (CMOS) and memristive technologies. First, the physics and operation of CMOS-based floating-gate memory devices in artificial neural networks will be addressed. Then, several memristive concepts will be reviewed and discussed for applications in deep neural network and spiking neural network architectures. Finally, the main technology challenges and perspectives of neuromorphic computing will be discussed.

A Case for 3D Integrated System Design for Neuromorphic Computing and AI Applications

2020 ◽  
Vol 14 (04) ◽  
pp. 457-475
Author(s):  
Eren Kurshan ◽  
Hai Li ◽  
Mingoo Seok ◽  
Yuan Xie
Keyword(s):  
Human Brain ◽  
Scaling Laws ◽  
Three Dimensional ◽  
Implementation Process ◽  
Cost Effective ◽  
Integrated System ◽  
3D Integration ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Manufacturing Technologies

Over the last decade, artificial intelligence (AI) has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, three-dimensional (3D) integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefit the designs in the future.

scholarly journals Memristive Artificial Synapses for Neuromorphic Computing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Wen Huang ◽  
Xuwen Xia ◽  
Chen Zhu ◽  
Parker Steichen ◽  
Weidong Quan ◽  
...  
Keyword(s):  
Brain Function ◽  
Neuromorphic Computing ◽  
Von Neumann ◽  
Optical Signals ◽  
Synaptic Functions ◽  
Neuromorphic Systems ◽  
Von Neumann Architecture ◽  
The Brain ◽  
Stimulation Of ◽  
Working Mechanisms

AbstractNeuromorphic computing simulates the operation of biological brain function for information processing and can potentially solve the bottleneck of the von Neumann architecture. This computing is realized based on memristive hardware neural networks in which synaptic devices that mimic biological synapses of the brain are the primary units. Mimicking synaptic functions with these devices is critical in neuromorphic systems. In the last decade, electrical and optical signals have been incorporated into the synaptic devices and promoted the simulation of various synaptic functions. In this review, these devices are discussed by categorizing them into electrically stimulated, optically stimulated, and photoelectric synergetic synaptic devices based on stimulation of electrical and optical signals. The working mechanisms of the devices are analyzed in detail. This is followed by a discussion of the progress in mimicking synaptic functions. In addition, existing application scenarios of various synaptic devices are outlined. Furthermore, the performances and future development of the synaptic devices that could be significant for building efficient neuromorphic systems are prospected.

scholarly journals Integrated neuromorphic computing networks by artificial spin synapses and spin neurons

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Seungmo Yang ◽  
Jeonghun Shin ◽  
Taeyoon Kim ◽  
Kyoung-Woong Moon ◽  
Jaewook Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Experimental Studies ◽  
Proof Of Concept ◽  
Stripe Domain ◽  
Network Systems ◽  
Neuromorphic Computing ◽  
Spintronic Devices ◽  
Low Energy Consumption ◽  
Neural Network Hardware

AbstractOne long-standing goal in the emerging neuromorphic field is to create a reliable neural network hardware implementation that has low energy consumption, while providing massively parallel computation. Although diverse oxide-based devices have made significant progress as artificial synaptic and neuronal components, these devices still need further optimization regarding linearity, symmetry, and stability. Here, we present a proof-of-concept experiment for integrated neuromorphic computing networks by utilizing spintronics-based synapse (spin-S) and neuron (spin-N) devices, along with linear and symmetric weight responses for spin-S using a stripe domain and activation functions for spin-N. An integrated neural network of electrically connected spin-S and spin-N successfully proves the integration function for a simple pattern classification task. We simulate a spin-N network using the extracted device characteristics and demonstrate a high classification accuracy (over 93%) for the spin-S and spin-N optimization without the assistance of additional software or circuits required in previous reports. These experimental studies provide a new path toward establishing more compact and efficient neural network systems with optimized multifunctional spintronic devices.

scholarly journals 2022 roadmap on neuromorphic computing and engineering

2022 ◽  
Author(s):  
Dennis Valbjørn Christensen ◽  
Regina Dittmann ◽  
Bernabe Linares-Barranco ◽  
Abu Sebastian ◽  
Manuel Le Gallo ◽  
...  
Keyword(s):  
Power Consumption ◽  
Data Transfer ◽  
Research Area ◽  
Complex Data ◽  
Digital Information ◽  
Neuromorphic Computing ◽  
Neuromorphic Circuits ◽  
Von Neumann ◽  
The Future ◽  
Von Neumann Architecture

Abstract Modern computation based on the von Neumann architecture is today a mature cutting-edge science. In the Von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this Roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The Roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this Roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

scholarly journals Graphene-based 3D XNOR-VRRAM with ternary precision for neuromorphic computing

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Batyrbek Alimkhanuly ◽  
Joon Sohn ◽  
Ik-Joon Chang ◽  
Seunghyun Lee
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Recognition Accuracy ◽  
Material Selection ◽  
Weighted Sum ◽  
Device Design ◽  
Key Factors ◽  
Neuromorphic Computing ◽  
Device Scaling ◽  
The Impact

AbstractRecent studies on neural network quantization have demonstrated a beneficial compromise between accuracy, computation rate, and architecture size. Implementing a 3D Vertical RRAM (VRRAM) array accompanied by device scaling may further improve such networks’ density and energy consumption. Individual device design, optimized interconnects, and careful material selection are key factors determining the overall computation performance. In this work, the impact of replacing conventional devices with microfabricated, graphene-based VRRAM is investigated for circuit and algorithmic levels. By exploiting a sub-nm thin 2D material, the VRRAM array demonstrates an improved read/write margins and read inaccuracy level for the weighted-sum procedure. Moreover, energy consumption is significantly reduced in array programming operations. Finally, an XNOR logic-inspired architecture designed to integrate 1-bit ternary precision synaptic weights into graphene-based VRRAM is introduced. Simulations on VRRAM with metal and graphene word-planes demonstrate 83.5 and 94.1% recognition accuracy, respectively, denoting the importance of material innovation in neuromorphic computing.

scholarly journals A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1526 ◽  
Author(s):  
Choongmin Kim ◽  
Jacob A. Abraham ◽  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Performance ◽  
Fixed Number ◽  
Decomposition Algorithm ◽  
Computing Systems ◽  
Network Decomposition ◽  
Von Neumann ◽  
Analog Computing ◽  
The Neural Network

Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons.

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