Empowering Malware Detection Efficiency within Processing-in-Memory Architecture

TL;DR

This paper introduces a Processing-in-Memory architecture with precision scaling to improve the efficiency and energy consumption of malware detection models based on CNNs, addressing resource challenges in real-time embedded systems.

Contribution

It proposes a novel PIM-based architecture with precision scaling techniques that significantly enhances throughput and energy efficiency for malware detection.

Findings

1.09x higher throughput compared to LUT-based PIM architectures

Energy efficiency improved by 1.5x with precision scaling

Maintains performance while reducing resource consumption

Abstract

The widespread integration of embedded systems across various industries has facilitated seamless connectivity among devices and bolstered computational capabilities. Despite their extensive applications, embedded systems encounter significant security threats, with one of the most critical vulnerabilities being malicious software, commonly known as malware. In recent times, malware detection techniques leveraging Machine Learning have gained popularity. Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) have proven particularly efficient in image processing tasks. However, one major drawback of neural network architectures is their substantial computational resource requirements. Continuous training of malware detection models with updated malware and benign samples demands immense computational resources, presenting a challenge for real-world applications. In response to these concerns, we propose a Processing-in-Memory (PIM)-based architecture to mitigate memory access latency, thereby reducing the resources consumed during model updates. To further enhance throughput and minimize energy consumption, we incorporate precision scaling techniques tailored for CNN models. Our proposed PIM architecture exhibits a 1.09x higher throughput compared to existing Lookup Table (LUT)-based PIM architectures. Additionally, precision scaling combined with PIM enhances energy efficiency by 1.5x compared to full-precision operations, without sacrificing performance. This innovative approach offers a promising solution to the resource-intensive nature of malware detection model updates, paving the way for more efficient and sustainable cybersecurity practices.