Approximate Computing for Spiking Neural Networks

Sanchari Sena, Swagath Venkataramanib and Anand Raghunathanc
School of Electrical and Computer Engineering, Purdue University.


Spiking Neural Networks (SNNs) are widely regarded as the third generation of artificial neural networks, and are expected to drive new classes of recognition, data analytics and computer vision applications. However, large-scale SNNs (e.g., of the scale of the human visual cortex) are highly compute and data intensive, requiring new approaches to improve their efficiency. Complementary to prior efforts that focus on parallel software and the design of specialized hardware, we propose AxSNN, the first effort to apply approximate computing to improve the computational efficiency of evaluating SNNs.
In SNNs, the inputs and outputs of neurons are encoded as a time series of spikes. A spike at a neuron's output triggers updates to the potentials (internal states) of neurons to which it is connected. AxSNN determines spike-triggered neuron updates that can be skipped with little or no impact on output quality and selectively skips them to improve both compute and memory energy. Neurons that can be approximated are identified by utilizing various static and dynamic parameters such as the average spiking rates and current potentials of neurons, and the weights of synaptic connections. Such a neuron is placed into one of many approximation modes, wherein the neuron is sensitive only to a subset of its inputs and sends spikes only to a subset of its outputs. A controller periodically updates the approximation modes of neurons in the network to achieve energy savings with minimal loss in quality. We apply AxSNN to both hardware and software implementations of SNNs. For hardware evaluation, we designed SNNAP, a Spiking Neural Network Approximate Processor that embodies the proposed approximation strategy, and synthesized it to 45nm technology. The software implementation of AxSNN was evaluated on a 2.7 GHz Intel Xeon server with 128 GB memory. Across a suite of 6 image recognition benchmarks, AxSNN achieves 1.4-5.538215reduction in scalar operations for network evaluation, which translates to 1.2-3.6238215; and 1.26-3.938215; improvement in hardware and software energies respectively, for no loss in application quality. Progressively higher energy savings are achieved with modest reductions in output quality.

Keywords: Approximate computing, Spiking neural networks, Approximate neural networks.

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