Spike-based neuromorphic hardware holds the promise to provide more energy efficient implementations of Deep Neural Networks (DNNs) than standard hardware such as GPUs. But this requires to understand how DNNs can be emulated in an event-based sparse firing regime, since otherwise the energy-advantage gets lost. In particular, DNNs that solve sequence processing tasks typically employ Long Short-Term Memory (LSTM) units that are hard to emulate with few spikes. We show that a facet of many biological neurons, slow after-hyperpolarizing (AHP) currents after each spike, provides an efficient solution. AHP-currents can easily be implemented in neuromorphic hardware that supports multi-compartment neuron models, such as Intel's Loihi chip. Filter approximation theory explains why AHP-neurons can emulate the function of LSTM units. This yields a highly energy-efficient approach to time series classification. Furthermore it provides the basis for implementing with very sparse firing an important class of large DNNs that extract relations between words and sentences in a text in order to answer questions about the text.
In this work we explore recurrent representations of leaky integrate and fire neurons operating at a timescale equal to their absolute refractory period. Our coarse time scale approximation is obtained using a probability distribution function for spike arrivals that is homogeneously distributed over this time interval. This leads to a discrete representation that exhibits the same dynamics as the continuous model, enabling efficient large scale simulations and backpropagation through the recurrent implementation. We use this approach to explore the training of deep spiking neural networks inc…
Finding spike-based learning algorithms that can be implemented within the local constraints of neuromorphic systems, while achieving high accuracy, remains a formidable challenge. Equilibrium Propagation is a promising alternative to backpropagation as it only involves local computations, but hardware-oriented studies have so far focused on rate-based networks. In this work, we develop a spiking neural network algorithm called EqSpike, compatible with neuromorphic systems, which learns by Equilibrium Propagation. Through simulations, we obtain a test recognition accuracy of 97.6% on MNIST, si…
Intelligent Science & Technology Academy of CASIC（中国航天科工集团智能科学与技术研究院）
Brain-inspired spiking neural networks (SNNs) have recently drawn more and more attention due to their event-driven and energy-efficient characteristics. The integration of storage and computation paradigm on neuromorphic hardwares makes SNNs much different from Deep Neural Networks (DNNs). In this paper, we argue that SNNs may not benefit from the weight-sharing mechanism, which can effectively reduce parameters and improve inference efficiency in DNNs, in some hardwares, and assume that an SNN with unshared convolution kernels could perform better. Motivated by this assumption, a training-in…
Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if and how they could be mapped onto neuromorphic hardware, and to compare their features and ease of implementation. To this end, in this survey, we provide a comprehensive overview of representative brain-inspired synaptic plasticity models a…
Neuromorphic Satellite Intelligence: A Formal Framework for Event-Driven Architectures, Spike-Based Algorithms, and Quantitative Energy-Accuracy Trade-offs
Spiking Neural Networks (SNN) are known to be very effective for neuromorphic processor implementations, achieving orders of magnitude improvements in energy efficiency and computational latency over traditional deep learning approaches. Comparable algorithmic performance was recently made possible as well with the adaptation of supervised training algorithms to the context of SNN. However, information including audio, video, and other sensor-derived data are typically encoded as real-valued signals that are not well-suited to SNN, preventing the network from leveraging spike timing informatio…
Neuromorphic Wireless Split Computing with Multi-Level Spikes
King’s Laboratory for Intelligent Computing (KLIC) lab within the Centre for Intelligent Information Processing Systems (CIIPS) at the Department of Engineering, King’s College London（国王智能计算实验室（KLIC）内位于智能信息处理系统中心（CIIPS）的工程系，伦敦国王学院）；King’s Communications, Learning and Information Processing (KCLIP) lab within the CIIPS at the Department of Engineering, King’s College London（国王通讯、学习和信息处理（KCLIP）实验室，位于智能信息处理系统中心（CIIPS）的工程系，伦敦国王学院）；Department of Electrical and Computer Engineering, Princeton University（普林斯顿大学电气与计算机工程系）
Energy-Aware Spike Budgeting for Continual Learning in Spiking Neural Networks for Neuromorphic Vision
Dept. of Electrical and Electronics Engineering, University of Liberal Arts Bangladesh（乌拉尔大学 bangladesh 电子与电气工程系）；Dept. of Electrical Engineering, Pennsylvania State University（宾夕法尼亚州立大学电气工程系）
SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence
Peking University（北京大学）；Peng Cheng Laboratory（鹏城实验室）；Shanghai Jiao Tong University（上海交通大学）；Chinese Academy of Sciences（中国科学院）；Centre de Recherche Cerveau et Cognition (CERCO), UMR5549 CNRS - Univ. Toulouse 3（大脑与认知研究中心（CERCO），CNRS UMR5549 - 图卢兹第三大学）；University of Chinese Academy of Sciences（中国科学院大学）
University of California San Diego（加利福尼亚大学圣地亚哥分校）；University of California Los Angeles（加利福尼亚大学洛杉矶分校）；University of Connecticut（康涅狄格大学）；Daegu Gyeongbuk Institute of Science and Technology（大邱庆北科学技术院）；University of California Irvine（加利福尼亚大学欧文分校）
Recently, brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivat…
University of Tennessee（田纳西大学）；Princeton University（普林斯顿大学）；Purdue University（普渡大学）
The revolution in artificial intelligence (AI) brings up an enormous storage and data processing requirement. Large power consumption and hardware overhead have become the main challenges for building next-generation AI hardware. To mitigate this, Neuromorphic computing has drawn immense attention due to its excellent capability for data processing with very low power consumption. While relentless research has been underway for years to minimize the power consumption in neuromorphic hardware, we are still a long way off from reaching the energy efficiency of the human brain. Furthermore, desig…
Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips, when coupled with spike-based sensors, can inherently adapt to the "semantics" of the data distribution by consuming energy only when relevant events are recorded in the timing of spikes and by proving a low-latency response to changing conditions in the environment. This paper proposes an end-to-end design for a neuromorphic wireless Internet-of-Things system that integrates spike-based sensing, processing, and com…
Spiking neural networks have shown great promise for the design of low-power sensory-processing and edge-computing hardware platforms. However, implementing on-chip learning algorithms on such architectures is still an open challenge, especially for multi-layer networks that rely on the back-propagation algorithm. In this paper, we present a spike-based learning method that approximates back-propagation using local weight update mechanisms and which is compatible with mixed-signal analog/digital neuromorphic circuits. We introduce a network architecture that enables synaptic weight update mech…
Neuromorphic hardware is based on emulating the natural biological structure of the brain. Since its computational model is similar to standard neural models, it could serve as a computational acceleration for research projects in the field of neuroscience and artificial intelligence, including biomedical applications. However, in order to exploit this new generation of computer chips, rigorous simulation and consequent validation of brain-based experimental data is imperative. In this work, we investigate the potential of Intel's fifth generation neuromorphic chip - `Loihi', which is based on…
As the technology industry is moving towards implementing tasks such as natural language processing, path planning, image classification, and more on smaller edge computing devices, the demand for more efficient implementations of algorithms and hardware accelerators has become a significant area of research. In recent years, several edge deep learning hardware accelerators have been released that specifically focus on reducing the power and area consumed by deep neural networks (DNNs). On the other hand, spiking neural networks (SNNs) which operate on discrete time-series data, have been show…
EG-SpikeFormer: Eye-Gaze Guided Transformer on Spiking Neural Networks for Medical Image Analysis
School of Computing, University of Georgia（佐治亚大学计算学院）；University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；University of Georgia（佐治亚大学）；Department of Physics and Astronomy（物理与天文学系）
Although widely used in machine learning, backpropagation cannot directly be applied to SNN training and is not feasible on a neuromorphic processor that emulates biological neuron and synapses. This work presents a spike-based backpropagation algorithm with biological plausible local update rules and adapts it to fit the constraint in a neuromorphic hardware. The algorithm is implemented on Intel Loihi chip enabling low power in-hardware supervised online learning of multilayered SNNs for mobile applications. We test this implementation on MNIST, Fashion-MNIST, CIFAR-10 and MSTAR datasets wit…
Spiking Neural Networks (SNN) are an emerging computation model, which uses event-driven activation and bio-inspired learning algorithms. SNN-based machine-learning programs are typically executed on tile- based neuromorphic hardware platforms, where each tile consists of a computation unit called crossbar, which maps neurons and synapses of the program. However, synthesizing such programs on an off-the-shelf neuromorphic hardware is challenging. This is because of the inherent resource and latency limitations of the hardware, which impact both model performance, e.g., accuracy, and hardware p…
A Neuromorphic VLSI Design for Spike Timing and Rate Based Synaptic Plasticity
基于三联体的尖峰时间依赖可塑性（TSTDP）是一种强大的突触可塑性规则，其作用超出了传统的基于对的STDP（PSTDP）。在这里，TSTDP能够再现各种生物实验的结果，而PSTDP规则无法再现它们。此外，它已被证明，固有的行为，以尖峰率为基础的Bienenstock-Cooper-Munro（B-M）突触可塑性规则也可以出现从TSTDP规则。本文提出了一种模拟实现的TSTDP规则。建议的超大规模集成电路设计使用AMS 0.35 μ m CMOS工艺，并已使用Synopsys和Cadence工具的设计套件进行了模拟。仿真结果表明，如何以及所提出的电路可以改变突触的权重，根据一组不同的尖峰模式之间的定时差。此外，该电路被示出产生一个类似于BCM的学习规则，这是一个基于速率的规则。为了模拟实现环境，对所提出的电路进行了1000次运行的蒙特卡罗（MC）分析。所提出的MC仿真分析和微调电路的仿真结果表明，这是可能的，以减轻工艺变化的影响，在概念验证电路，然而，一个实际的变化意识的设计技术，需要保证高的电路性能在大规模神经网络。我们相信，所提出的设计可以发挥重要作用，在未来的超大规模集成电路实现的尖峰定时和基于速率的神经形态学习系统。
