  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.
