# Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units

Source: [Towards a Design Framework for TNN-Based Neuromorphic Sensory Processing Units](<https://www.sota2.com/research/paper/68fa6ca71e209131cea35482>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:15:46.115Z

Authors: Prabhu Vellaisamy, John Paul Shen

Published: 2022-05-27T21:51:05.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2205.14248>)

[PDF](<https://arxiv.org/pdf/2205.14248.pdf>)