A 1.52 pJ/Spike Reconfigurable Multimodal Integrate-and-Fire Neuron Array Transceiver
| Autor: | Rajkumar Kubendran, Siddharth Joshi, H.-S. Philip Wong, Gert Cauwenberghs, Weier Wan |
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| Rok vydání: | 2020 |
| Předmět: |
business.industry
Computer science Deep learning Boltzmann machine 02 engineering and technology Sigmoid function Rectifier (neural networks) Convolutional neural network 020202 computer hardware & architecture medicine.anatomical_structure CMOS Neuromorphic engineering 0202 electrical engineering electronic engineering information engineering medicine Artificial intelligence Neuron Transceiver business MNIST database Computer hardware |
| Zdroj: | ICONS |
| DOI: | 10.1145/3407197.3407209 |
| Popis: | 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%. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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