3DRA: Dynamic Data-Driven Reconfigurable Architecture - Lee, Jinho; Amornpaisannon, Burin; Diavastos, Andreas; Carlson, Trevor E. (My University, 2023)
Specialized accelerators are becoming a standard way to achieve both high-performance and efficient computation. We see this trend extending to all areas of computing, from low-power edge-computing systems to high-performance processors in datacenters. Reconfigurable architectures, such as Coarse-Grained Reconfigurable Arrays (CGRAs), attempt to find a balance between performance and energy efficiency by trading off dynamism, flexibility, and programmability. Our goal in this work is to find a new solution that provides the flexibility of traditional CPUs, with the parallelism of a CGRA, to improve overall performance and energy efficiency. Our design, the Dynamic Data-Driven Reconfigurable Architecture (3DRA), is unique, in that it targets both low-latency and high-throughput workloads. This architecture implements a dynamic dataflow execution model that resolves data dependencies at run-time and utilizes non-blocking broadcast communication that reduces transmission latency to a single cycle to achieve high performance and energy efficiency. By employing a dynamic model, 3DRA eliminates costly mapping algorithms during compilation and improves the flexibility and compilation time of traditional CGRAs. The 3DRA architecture achieves up to 731MIPS/mW, and it improves performance by up to 4.43x compared to the current state-of-the-art CGRA-based accelerators.
Keywords: CGRA; dynamic dataflow; Reconfigurable architectures; coarse-grained reconfigurable array; accelerators