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A Fully-Configurable Digital Spiking Neuromorphic Hardware Design with Variable Quantization and Mixed Precision
Conference proceeding

A Fully-Configurable Digital Spiking Neuromorphic Hardware Design with Variable Quantization and Mixed Precision

Shadi Matinizadeh, Arghavan Mohammadhassani, Noah Pacik-Nelson, Ioannis Polykretisl, Abhishek Mishra, James Shackleford, Nagarajan Kandasamy, Eric Gallo and Anup Das
2024 IEEE 67th International Midwest Symposium on Circuits and Systems (MWSCAS), pp 937-941
11 Aug 2024
DOI: https://doi.org/10.1109/MWSCAS60917.2024.10658724

Abstract

Hardware mixed precision neuromorphic computing Neuromorphics Power demand Quantization (signal) Spiking neural networks spiking neural networks (SNNs) Training variable quantization Computer Hardware Neurons
We introduce QUANTISENC, a fully-configurable digital spiking neuromorphic hardware to optimize performance and power consumption of spiking neural networks (SNNs). QUANTISENC introduces two key contributions. First, it allows the user to set separate quantization and precision policies for the synaptic weights and the internal state variables of neurons to optimize the design based on the precision needed for a target SNN model and the dataset used for training. This reduces the quantization error. Second, in addition to using static design parameters, QUANTISENC also allows to dynamically configure neuron parameters via programming its configuration registers. This allows the user to fine-tune performance and power consumption even after a design is implemented on silicon. Using open-source datasets, we show improvement in area, power, and performance over several state-of-the-art designs.

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