A 1.52 pJ/Spike Reconfigurable Multimodal Integrate-and-Fire Neuron Array Transceiver
A 1.52 pJ/Spike Reconfigurable Multimodal Integrate-and-Fire Neuron Array Transceiver
复制标题
1.52 pJ/Spike 可重构多模态集成发射神经元阵列收发器
DOI：
10.1145/3407197.3407209
复制
发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Cauwenberghs, Gert
中科院分区：
文献类型：
--
作者：
Kubendran, Rajkumar;Wan, Weier;Joshi, Siddharth;Wong, H.-S. Philip;Cauwenberghs, Gert
提出了一种具有多模态神经元架构的超低功耗集成和发射神经元阵列收发器。该设计具有 16 × 16 电荷模式混合信号神经元阵列，可配置为实现各种激活函数，包括阶跃、S 形和整流线性单元 (ReLU)，通过电荷累积中的部分复位重新配置时钟波形，并通过线性反馈移位寄存器 (LFSR) 耦合添加随机噪声。神经元输出基于尖峰的稀疏同步事件，这些事件可以是二元（事件/无事件）或三元（正/负/无事件）。可重新配置的节能设计使该架构适用于深度学习和神经形态应用，例如受限玻尔兹曼机、卷积神经网络和通用事件驱动计算。采用 130nm CMOS 技术制造的 1.796 mm2 芯片在 1.8V 电源下以 92.5 MSpikes/s 消耗 140.6 μW，实现 1.52 pJ/Spike 的能效品质因数 (FoM)。使用 sigmoid 和 ReLU 激活在芯片上实现的 CNN 架构实现了 MNIST 预测准确率 94.8% 和 96.9%。
An ultra-low power integrate-and-fire neuron array transceiver with a multi-modal neuron architecture is presented. The design features an array of 16 × 16 charge-mode mixed-signal neurons that can be configured to implement a variety of activation functions, including step, sigmoid and Rectified Linear Unit (ReLU), through reconfiguration of clocking waveforms through partial reset in charge accumulation and additive stochastic noise by Linear Feedback Shift Register (LFSR) coupling. The neuron outputs spike-based sparse synchronous events, which are either binary (event/no event) or ternary (positive/negative/no events). The reconfigurable energy-efficient design makes this architecture suitable for deep learning and neuromorphic applications like Restricted Boltzmann Machines, Convolutional Neural Networks and general event-driven computing. The 1.796 mm2 chip fabricated in 130nm CMOS technology consumes 140.6 μW from a 1.8V supply at 92.5 MSpikes/s achieving an energy efficiency Figure-of-Merit (FoM) of 1.52 pJ/Spike. A CNN architecture implemented on the chip using sigmoid and ReLU activation achieves MNIST prediction accuracy of 94.8% and 96.9%.
影响因子：
20.6
作者：
Neckar, Alexander;Fok, Sam;Boahen, Kwabena
通讯作者：
Boahen, Kwabena
影响因子：
64.8
作者：
Pei, Jing;Deng, Lei;Shi, Luping
通讯作者：
Shi, Luping