OpenGeMM: A High-Utilization GeMM Accelerator Generator with Lightweight RISC-V Control and Tight Memory Coupling (2411.09543v2)

Abstract: Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While standalone accelerators tailored for specific application scenarios suffer from inflexible control and limited programmability, generic hardware acceleration platforms coupled with RISC-V CPUs can enable high reusability and flexibility, yet typically at the expense of system level efficiency and low utilization. To fill this gap, we propose OpenGeMM, an open-source acceleration platform, jointly demonstrating high efficiency and utilization, as well as ease of configurability and programmability. OpenGeMM encompasses a parameterized Chisel-coded GeMM accelerator, a lightweight RISC-V processor, and a tightly coupled multi-banked scratchpad memory. The GeMM core utilization and system efficiency are boosted through three mechanisms: configuration pre-loading, input pre-fetching with output buffering, and programmable strided memory access. Experimental results show that OpenGeMM can consistently achieve hardware utilization ranging from 81.89% to 99.34% across diverse CNN and Transformer workloads. Compared to the SotA open-source Gemmini accelerator, OpenGeMM demonstrates a 3.58x to 16.40x speedup on normalized throughput across a wide variety ofGeMM workloads, while achieving 4.68 TOPS/W system efficiency.

• The paper presents a high-efficiency GeMM accelerator that integrates lightweight RISC-V control with tightly coupled, multi-banked scratchpad memory, achieving 81.89%-99.34% hardware utilization.
• It employs configuration pre-loading, input pre-fetching with output buffering, and programmable strided memory access to deliver a 3.58× to 16.40× throughput improvement over the Gemmini accelerator.
• The study demonstrates a practical edge AI solution that balances efficiency and programmability, achieving 4.68 TOPS/W and supporting diverse deep neural network workloads.

OpenGeMM: An In-Depth Examination of a High-Efficiency GeMM Accelerator Generator

The paper "OpenGeMM: A High-Utilization GeMM Accelerator Generator with Lightweight RISC-V Control and Tight Memory Coupling" presents a significant advancement in the design and implementation of General Matrix Multiplication (GeMM) accelerators, particularly for deployment on resource-constrained edge devices. The authors address the crucial problem of balancing computational efficiency with flexibility in deep neural network (DNN) accelerators, considering the increasing demand for DNNs in edge applications like in-vehicle systems and wearable devices.

Overview and Contributions

OpenGeMM is proposed as a comprehensive, open-source platform that combines high-efficiency hardware utilization with a programmable and flexible architecture. It stands out through the integration of a parameterizable Chisel-coded GeMM accelerator, a lightweight RISC-V processor, and a tightly coupled multi-banked scratchpad memory system. The accelerator leverages three core mechanisms: configuration pre-loading, input pre-fetching with output buffering, and programmable strided memory access. This architecture allows sustained high hardware utilization, achieving between 81.89% and 99.34% utilization across a range of DNN workloads.

The authors highlight that OpenGeMM achieves significant throughput improvements when benchmarked against existing state-of-the-art solutions. Experimental results document a 3.58× to 16.40× speedup in normalized throughput over the open-source Gemmini accelerator. Notably, the system delivers a system efficiency of 4.68 TOPS/W.

Technical Insights

The paper delves deeply into the technical aspects underpinning this performance. Key among these is the layered dataflow design of the GeMM core, leveraging spatial and temporal data reuse to minimize idle cycles and memory access overhead. The architecture's configurability is pivotal, offering substantial opportunity for customization to fit varying workload demands. On the temporal side, output-stationary dataflow is implemented, reducing the memory transfer overhead by focusing data movement on input matrices.

Configuration Strategies: The introduction of a configuration pre-loading mechanism facilitates the overlapping of configuration time with execution time, effectively mitigating the potential delay caused by the configuration overhead in RISC-V environments.

Data Handling: By utilizing input data pre-fetching and an output data buffering mechanism, OpenGeMM enhances real-time data feeding to its compute units, minimizing stalls associated with memory accesses. This is critical for maintaining high utilization rates, especially in operations characterized by irregular access patterns.

Memory Management: The tightly coupled multi-banked SPM, featuring high bandwidth and programmable strided memory access, ensures that data transactions do not become a bottleneck, which is a prevalent issue in high-throughput matrix operations.

Implications and Future Directions

The implications of this research for the development of edge AI hardware are significant. OpenGeMM represents a robust means of mitigating the traditional trade-offs between efficiency and flexibility in accelerator design for diverse and computationally intensive AI applications. It exemplifies a practical approach to achieving high-performance computation while supporting programmability via standardized RISC-V instructions.

Moving forward, the open-source nature of OpenGeMM, coupled with its foundational flexibility, sets the groundwork for further explorations into optimized hardware-software co-design. Future developments could expand upon the configurable nature of the GeMM core to engage with progressively diverse and intricate AI models, including those involving sparse data or variable precision operations, without compromising on efficiency. Additionally, investigating the integration of OpenGeMM with emerging non-volatile memory technologies or advanced packaging solutions could offer new frontiers in energy-efficient AI processing for edge devices.

OpenGeMM thus not only fills a crucial gap in current accelerator platforms but also paves the way for continuous innovation in high-utilization, flexible hardware for next-generation AI workloads.