System Optimization for Coarse-Grained Reconfigurable Arrays

What is it about?

This study introduces a strategy to optimize Coarse-Grained Reconfigurable Arrays (CGRAs), addressing performance degradation caused by irregular memory access patterns. CGRAs are specialized hardware used to accelerate computational workloads but can struggle with irregular memory access in complex tasks like graph analytics and unstructured computations. By redesigning the CGRA and its memory subsystem, incorporating caches, and implementing pre-execution and custom optimizations, we effectively manage these irregular memory accesses. Our experiments demonstrate a significant reduction in storage requirements and a remarkable enhancement in memory access efficiency, achieving up to 6.91 times performance acceleration. This research offers a new approach to overcoming memory bottlenecks in complex computational workloads.

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Photo by Adi Goldstein on Unsplash

Photo by Adi Goldstein on Unsplash

Why is it important?

This research is crucial because it addresses a major hurdle in maximizing the efficiency of CGRAs. As artificial intelligence tasks, such as those involving Graph Neural Networks (GNNs), along with irregular database operations and unstructured high-performance computing tasks, demand more processing power, overcoming memory access challenges becomes vital. Our approach significantly reduces the need for large amounts of storage and boosts computing speed, offering a new way to tackle existing limitations and drastically improving performance.

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