Year: 2024
Type: article
Abstract: Recent advances in machine learning (ML) have spotlighted the pressing need for computing architectures that bridge the gap between memory bandwidth and processing power. The advent of deep neural networks has pushed traditional Von Neumann architectures to their limits due to the high latency and energy consumption costs associated with data movement between the processor and memory for these workloads. One of the solutions to overcome this bottleneck is to perform computation within the main memory through processing-in-memory (PIM), thereby limiting data movement and the costs associated with it. However, dynamic random-access memory-based PIM struggles to achieve high throughput and energy efficiency due to internal data movement bottlenecks and the need for frequent refresh operations. In this work, we introduce OPIMA, a PIM-based ML accelerator, architected within an optical main memory. OPIMA has been designed to leverage the inherent massive parallelism within main memory while performing high-speed, low-energy optical computation to accelerate ML models based on convolutional neural networks. We present a comprehensive analysis of OPIMA to guide design choices and operational mechanisms. In addition, we evaluate the performance and energy consumption of OPIMA, comparing it with conventional electronic computing systems and emerging photonic PIM architectures. The experimental results show that OPIMA can achieve$2.98\times $higher throughput and$137\times $better energy efficiency than the best known prior work.
Institution: Colorado State University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Science Foundation
Awards: SHF: Small: Energy-Efficient and Reliable Communication with Silicon Photonics for Terascale Datacenters-on-Chip, CAREER: Optimizing Scalability and Reconfigurability in Silicon Photonic Switch Fabrics, FET: Small: Design Optimization of Silicon Photonic Integrated Circuits under Fabrication Process Variations
OpenAlex ID: W4404100950