Fault Injection in Native Logic-in-Memory Computation on Neuromorphic Hardware

TL;DR

This paper introduces FLIM, a fault injection platform for logic-in-memory computing in neuromorphic hardware, significantly accelerating binary neural network simulations and enabling fault impact analysis.

Contribution

The paper presents FLIM, a novel fault injection platform for LIM-based BNNs, achieving high-speed simulation and detailed fault impact assessment.

Findings

FLIM runs a single MNIST image 66,754 times faster than previous methods.

FLIM provides fine-grained fault injection capabilities.

Fault injection impacts on BNN accuracy are analyzed.

Abstract

Logic-in-memory (LIM) describes the execution of logic gates within memristive crossbar structures, promising to improve performance and energy efficiency. Utilizing only binary values, LIM particularly excels in accelerating binary neural networks, shifting it in the focus of edge applications. Considering its potential, the impact of faults on BNNs accelerated with LIM still lacks investigation. In this paper, we propose faulty logic-in-memory (FLIM), a fault injection platform capable of executing full-fledged BNNs on LIM while injecting in-field faults. The results show that FLIM runs a single MNIST picture 66754x faster than the state of the art by offering a fine-grained fault injection methodology.