Few-Shot Testing: Estimating Uncertainty of Memristive Deep Neural Networks Using One Bayesian Test Vector

TL;DR

This paper introduces a Bayesian test vector framework for memristor-based neural networks that accurately estimates model uncertainty, enhancing confidence in predictions despite hardware non-idealities and resource constraints.

Contribution

It presents a novel Bayesian test vector generation method that is more generalizable and memory-efficient for memristor-based neural network hardware.

Findings

Achieves 100% coverage across various conditions.

Requires only 0.024 MB of memory overhead.

Works effectively with different model dimensions and fault rates.

Abstract

The performance of deep learning algorithms such as neural networks (NNs) has increased tremendously recently, and they can achieve state-of-the-art performance in many domains. However, due to memory and computation resource constraints, implementing NNs on edge devices is a challenging task. Therefore, hardware accelerators such as computation-in-memory (CIM) with memristive devices have been developed to accelerate the most common operations, i.e., matrix-vector multiplication. However, due to inherent device properties, external environmental factors such as temperature, and an immature fabrication process, memristors suffer from various non-idealities, including defects and variations occurring during manufacturing and runtime. Consequently, there is a lack of complete confidence in the predictions made by the model. To improve confidence in NN predictions made by hardware accelerators in the presence of device non-idealities, in this paper, we propose a Bayesian test vector generation framework that can estimate the model uncertainty of NNs implemented on memristor-based CIM hardware. Compared to the conventional point estimate test vector generation method, our method is more generalizable across different model dimensions and requires storing only one test Bayesian vector in the hardware. Our method is evaluated on different model dimensions, tasks, fault rates, and variation noise to show that it can consistently achieve $100\%$ coverage with only $0.024$ MB of memory overhead.