290 research outputs found
2022 roadmap on neuromorphic computing and engineering
Modern computation based on von Neumann architecture is now 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 10 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
Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks
In the rapid evolution of next-generation brain-inspired artificial
intelligence and increasingly sophisticated electromagnetic environment, the
most bionic characteristics and anti-interference performance of spiking neural
networks show great potential in terms of computational speed, real-time
information processing, and spatio-temporal information processing. Data
processing. Spiking neural network is one of the cores of brain-like artificial
intelligence, which realizes brain-like computing by simulating the structure
and information transfer mode of biological neural networks. This paper
summarizes the strengths, weaknesses and applicability of five neuronal models
and analyzes the characteristics of five network topologies; then reviews the
spiking neural network algorithms and summarizes the unsupervised learning
algorithms based on synaptic plasticity rules and four types of supervised
learning algorithms from the perspectives of unsupervised learning and
supervised learning; finally focuses on the review of brain-like neuromorphic
chips under research at home and abroad. This paper is intended to provide
learning concepts and research orientations for the peers who are new to the
research field of spiking neural networks through systematic summaries
Neuromorphic hardware for somatosensory neuroprostheses
In individuals with sensory-motor impairments, missing limb functions can be restored using neuroprosthetic devices that directly interface with the nervous system. However, restoring the natural tactile experience through electrical neural stimulation requires complex encoding strategies. Indeed, they are presently limited in effectively conveying or restoring tactile sensations by bandwidth constraints. Neuromorphic technology, which mimics the natural behavior of neurons and synapses, holds promise for replicating the encoding of natural touch, potentially informing neurostimulation design. In this perspective, we propose that incorporating neuromorphic technologies into neuroprostheses could be an effective approach for developing more natural human-machine interfaces, potentially leading to advancements in device performance, acceptability, and embeddability. We also highlight ongoing challenges and the required actions to facilitate the future integration of these advanced technologies
Analog Spiking Neuromorphic Circuits and Systems for Brain- and Nanotechnology-Inspired Cognitive Computing
Human society is now facing grand challenges to satisfy the growing demand for computing power, at the same time, sustain energy consumption. By the end of CMOS technology scaling, innovations are required to tackle the challenges in a radically different way. Inspired by the emerging understanding of the computing occurring in a brain and nanotechnology-enabled biological plausible synaptic plasticity, neuromorphic computing architectures are being investigated. Such a neuromorphic chip that combines CMOS analog spiking neurons and nanoscale resistive random-access memory (RRAM) using as electronics synapses can provide massive neural network parallelism, high density and online learning capability, and hence, paves the path towards a promising solution to future energy-efficient real-time computing systems. However, existing silicon neuron approaches are designed to faithfully reproduce biological neuron dynamics, and hence they are incompatible with the RRAM synapses, or require extensive peripheral circuitry to modulate a synapse, and are thus deficient in learning capability. As a result, they eliminate most of the density advantages gained by the adoption of nanoscale devices, and fail to realize a functional computing system.
This dissertation describes novel hardware architectures and neuron circuit designs that synergistically assemble the fundamental and significant elements for brain-inspired computing. Versatile CMOS spiking neurons that combine integrate-and-fire, passive dense RRAM synapses drive capability, dynamic biasing for adaptive power consumption, in situ spike-timing dependent plasticity (STDP) and competitive learning in compact integrated circuit modules are presented. Real-world pattern learning and recognition tasks using the proposed architecture were demonstrated with circuit-level simulations. A test chip was implemented and fabricated to verify the proposed CMOS neuron and hardware architecture, and the subsequent chip measurement results successfully proved the idea.
The work described in this dissertation realizes a key building block for large-scale integration of spiking neural network hardware, and then, serves as a step-stone for the building of next-generation energy-efficient brain-inspired cognitive computing systems
Memristors -- from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven most
of the recent fundamental developments in artificial intelligence. Deep
learning is based on computational models that are, to a certain extent,
bio-inspired, as they rely on networks of connected simple computing units
operating in parallel. Deep learning has been successfully applied in areas
such as object/pattern recognition, speech and natural language processing,
self-driving vehicles, intelligent self-diagnostics tools, autonomous robots,
knowledgeable personal assistants, and monitoring. These successes have been
mostly supported by three factors: availability of vast amounts of data,
continuous growth in computing power, and algorithmic innovations. The
approaching demise of Moore's law, and the consequent expected modest
improvements in computing power that can be achieved by scaling, raise the
question of whether the described progress will be slowed or halted due to
hardware limitations. This paper reviews the case for a novel beyond CMOS
hardware technology, memristors, as a potential solution for the implementation
of power-efficient in-memory computing, deep learning accelerators, and spiking
neural networks. Central themes are the reliance on non-von-Neumann computing
architectures and the need for developing tailored learning and inference
algorithms. To argue that lessons from biology can be useful in providing
directions for further progress in artificial intelligence, we briefly discuss
an example based reservoir computing. We conclude the review by speculating on
the big picture view of future neuromorphic and brain-inspired computing
systems.Comment: Keywords: memristor, neuromorphic, AI, deep learning, spiking neural
networks, in-memory computin
modeling and simulation of spiking neural networks with resistive switching synapses
Artificial intelligence (AI) has recently reached excellent achievements in the implementation of human brain cognitive functions such as learning, recognition and inference by running intensively neural networks with deep learning on high-performance computing platforms. However, excessive computational time and power consumption required for achieving such performance make AI inefficient compared with human brain. To replicate the efficient operation of human brain in hardware, novel nanoscale memory devices such as resistive switching random access memory (RRAM) have attracted strong interest thanks to their ability to mimic biological learning in silico. In this chapter, design, modeling and simulation of RRAM-based electronic synapses capable of emulating biological learning rules are first presented. Then, the application of RRAM synapses in spiking neural networks to achieve neuromorphic tasks such as on-line learning of images and associative learning is addressed
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