MIDAS: A Mapping Infrastructure for Configurable, Data-Streaming Based Domain Specific Accelerators

• Martim Bento INESC-ID / Instituto Superior Técnico / Universidade de Lisboa
• Nuno Neves INESC-ID / Instituto Superior Técnico / Universidade de Lisboa
• Pedro Tomás INESC-ID / Instituto Superior Técnico / Universidade de Lisboa
• Nuno Roma INESC-ID / Instituto Superior Técnico / Universidade de Lisboa

Resumo

As computational demands continue to grow in key application domains, Domain-Specific Accelerators (DSAs) have become a promising solution for delivering high performance with improved area and energy efficiency. However, traditional memory address generation in DSAs often consumes valuable resources that could otherwise be used to enhance performance. Data streaming mechanisms address this issue by eliminating the need for address generation nodes, but the exploitation of this paradigm remains underutilized in existing DSA design toolchains. On the other hand, despite fast kernel mapping and early feedback metrics being regarded as critical features in design space exploration for DSAs, most publicly available toolchains are either too slow, provide feedback only after a full compilation, or lack direct support for data streaming. This paper introduces a new Mapping Infrastructure for Data-Streaming-Based Accelerators (MIDAS) designed to rapidly map kernels onto configurable processing arrays equipped with an integrated streaming engine. When used as a co-design exploration tool, it provides early feedback on multiple metrics, facilitating architectural optimisation and pruning, and leading to a considerable improvement of hardware and energy efficiency. Moreover, the compute-only Data Flow Graphs (DFGs) are also more amenable to vectorisation, enabling further performance gains from data streaming. The obtained experimental results demonstrate that MIDAS achieves mapping speeds up to 73x faster than state-of-the-art tools like CGRA-ME’s CLUMAP, while maintaining competitive mapping quality. Under the data streaming paradigm, the implemented array achieves up to 8x performance improvement on a 4x4 PE array configuration compared to conventional architectures, with significantly reduced resource usage.

Palavras-chave: Measurement, High performance computing, Performance gain, Parallel processing, Energy efficiency, Space exploration, Reconfigurable architectures, Arrays, Kernel, Optimization, Reconfigurable Architectures, Data Streaming, Toolchain, Mapping