Hybrid Digital/Analog In-Memory Computing

Abstract

The relentless advancement of deep learning applications, particularly the highly potent yet computationally intensive deep unsupervised learning models, is pushing the boundaries of what modern general-purpose CPUs and GPUs can handle in terms of computation, communication, and storage capacities. To meet these burgeoning memory and computational demands, computing systems based on in-memory computing, which extensively utilize accelerators, are emerging as the next frontier in computing technology. This thesis delves into my research efforts aimed at overcoming these obstacles to develop a processing-in-memory based computing system tailored for machine learning tasks, with a focus on employing a hybrid digital/analog design approach.In the initial part of my work, I introduce a novel concept that leverages hybrid digital/analog in-memory computing to enhance the efficiency of depth-wise convolution applications. This approach not only optimizes computational efficiency but also paves the way for more energy-efficient machine learning operations.Following this, I expand upon the initial concept by presenting a design methodology that applies hybrid digital/analog in-memory computing to the processing of sparse attention operators. This extension significantly improves mapping efficiency, making it a vital enhancement for the processing capabilities of deep learning models that rely heavily on attention mechanisms.In my third piece of work, I detail the implementation strategies aimed at augmenting the power efficiency of in-memory computing macros. By integrating hybrid digital/analog computing concepts, this implementation focuses on general-purpose neural network acceleration, showcasing a significant step forward in reducing the energy consumption of such computational processes.Lastly, I introduce a system-level simulation tool designed for simulating general-purpose in-memory-computing based systems. This tool facilitates versatile architecture exploration, allowing for the assessment and optimization of various configurations to meet the specific needs of machine learning workloads. Through these comprehensive research efforts, this thesis contributes to the advancement of in-memory computing technologies, offering novel solutions to the challenges posed by the next generation of machine learning applications.