A new toolchain automatically converts PyTorch temporal neural networks into post-layout chip designs, with forecasting equations for die area and leakage power.
Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units
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abstract
Temporal Neural Networks (TNNs) are spiking neural networks that exhibit brain-like sensory processing with high energy efficiency. This work presents the ongoing research towards developing a custom design framework for designing efficient application-specific TNN-based Neuromorphic Sensory Processing Units (NSPUs). This paper examines previous works on NSPU designs for UCR time-series clustering and MNIST image classification applications. Current ideas for a custom design framework and tools that enable efficient software-to-hardware design flow for rapid design space exploration of application-specific NSPUs while leveraging EDA tools to obtain post-layout netlist and power-performance-area (PPA) metrics are described. Future research directions are also outlined.
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TNNGen: Automated Design of Neuromorphic Sensory Processing Units for Time-Series Clustering
A new toolchain automatically converts PyTorch temporal neural networks into post-layout chip designs, with forecasting equations for die area and leakage power.