Hessian Aware Quantization of Spiking Neural Networks

• URL: http://arxiv.org/abs/2104.14117v2
• Date: Mon, 23 Aug 2021 18:08:01 GMT
• Title: Hessian Aware Quantization of Spiking Neural Networks
• Authors: Hin Wai Lui and Emre Neftci
• Abstract summary: Neuromorphic architecture allows massively parallel computation with variable and local bit-precisions. Current gradient based methods of SNN training use a complex neuron model with multiple state variables. We present a simplified neuron model that reduces the number of state variables by 4-fold while still being compatible with gradient based training.
• Score: 1.90365714903665
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: To achieve the low latency, high throughput, and energy efficiency benefits of Spiking Neural Networks (SNNs), reducing the memory and compute requirements when running on a neuromorphic hardware is an important step. Neuromorphic architecture allows massively parallel computation with variable and local bit-precisions. However, how different bit-precisions should be allocated to different layers or connections of the network is not trivial. In this work, we demonstrate how a layer-wise Hessian trace analysis can measure the sensitivity of the loss to any perturbation of the layer's weights, and this can be used to guide the allocation of a layer-specific bit-precision when quantizing an SNN. In addition, current gradient based methods of SNN training use a complex neuron model with multiple state variables, which is not ideal for compute and memory efficiency. To address this challenge, we present a simplified neuron model that reduces the number of state variables by 4-fold while still being compatible with gradient based training. We find that the impact on model accuracy when using a layer-wise bit-precision correlated well with that layer's Hessian trace. The accuracy of the optimal quantized network only dropped by 0.2%, yet the network size was reduced by 58%. This reduces memory usage and allows fixed-point arithmetic with simpler digital circuits to be used, increasing the overall throughput and energy efficiency.

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