A Power-Efficient Binary-Weight Spiking Neural Network Architecture for Real-Time Object Classification

BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning

Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the need to perform multiplications. An alternative, emerging, approach relies on the use of Spiking Neural Networks (SNNs), biologically inspired, dynamic, event-driven models that enhance energy efficiency via the use of binary, sparse, activations. In this paper, an SNN model is introduced that combines the benefits of temporally sparse binary activations and of binary weights. Two learning rules are derived, the fir…

Spiking Neural Networks -- Part I: Detecting Spatial Patterns

Spiking Neural Networks (SNNs) are biologically inspired machine learning models that build on dynamic neuronal models processing binary and sparse spiking signals in an event-driven, online, fashion. SNNs can be implemented on neuromorphic computing platforms that are emerging as energy-efficient co-processors for learning and inference. This is the first of a series of three papers that introduce SNNs to an audience of engineers by focusing on models, algorithms, and applications. In this first paper, we first cover neural models used for conventional Artificial Neural Networks (ANNs) and SN…

VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits

Networks of spiking neurons and Winner-Take-All spiking circuits (WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing of events of interest, e.g., clicks, as well as by categorical numerical values assigned to each event, e.g., like or dislike. Other use cases include object recognition from data collected by neuromorphic cameras, which produce, for each pixel, signed bits at the times of sufficiently large brightness variations. Existing schemes for training WTA-SNNs are limited to rate-encoding solutions, and are hence able to de…

SNN-SC：一种面向协作智能的脉冲语义通信框架

SNN-SC: A Spiking Semantic Communication Framework for Collaborative Intelligence

本文提出SNN-SC脉冲语义通信框架，利用脉冲神经网络提取紧凑语义信息并通过数字二进制信道传输，结合IHF神经元和熵最大化，在协作智能中实现更高压缩比、更低计算复杂度并克服悬崖效应。

Wearable-based Human Activity Recognition with Spatio-Temporal Spiking Neural Networks

We study the Human Activity Recognition (HAR) task, which predicts user daily activity based on time series data from wearable sensors. Recently, researchers use end-to-end Artificial Neural Networks (ANNs) to extract the features and perform classification in HAR. However, ANNs pose a huge computation burden on wearable devices and lack temporal feature extraction. In this work, we leverage Spiking Neural Networks (SNNs)--an architecture inspired by biological neurons--to HAR tasks. SNNs allow spatio-temporal extraction of features and enjoy low-power computation with binary spikes. We conduc…

Time-domain digital-to-analog converter for spiking neural network hardware

We propose a new digital-to-analog converter (DAC) for realizing a synapse circuit of mixed-signal spiking neural networks. We named this circuit "time-domain DAC (TDAC)". This produces weights for converting a digital input code into voltage using one current waveform. Therefore, a TDAC is more compact than a conventional DAC that comprises many current sources and resistors. Moreover, a TDAC with leak resistance reproduces biological plausible synaptic responses that are expressed as alpha functions or dual exponential equations. We will show numerical analysis results of a TDAC and circuit …