Fast, Smart Neuromorphic Sensors Based on Heterogeneous Networks and Mixed Encodings

TL;DR

This paper presents a neuromorphic sensor design inspired by insect brains that uses heterogeneous networks and mixed encodings to achieve rapid input processing and hypothesis generation within a few cycles.

Contribution

It introduces a novel approach combining time and rate encodings in neuromorphic architectures for fast, smart sensing based on biological models.

Findings

Sensors can generate hypotheses in a few cycles

Heterogeneous networks improve processing speed

Mixed encodings enable rapid and detailed analysis

Abstract

Neuromorphic architectures are ideally suited for the implementation of smart sensors able to react, learn, and respond to a changing environment. Our work uses the insect brain as a model to understand how heterogeneous architectures, incorporating different types of neurons and encodings, can be leveraged to create systems integrating input processing, evaluation, and response. Here we show how the combination of time and rate encodings can lead to fast sensors that are able to generate a hypothesis on the input in only a few cycles and then use that hypothesis as secondary input for more detailed analysis.