Modern data-driven applications expose limitations of von Neumann architectures-extensive data movement, low throughput, and poor energy efficiency. Accelerators improve performance but lack flexibility and require data transfers. Existing compute in- and near-memory solutions mitigate these issues but face usability challenges due to data placement constraints. We propose a novel cache architecture that doubles as a tightly-coupled compute-near-memory coprocessor. Our RISC-V cache controller executes custom instructions from the host CPU using vector operations dispatched to near-memory vector processing units within the cache memory subsystem. This architecture abstracts memory synchronization and data mapping from application software while offering software-based Instruction Set Architecture extensibility. Our implementation shows 30x to 84x performance improvement when operating on 8-bit data over the same system with a traditional cache when executing a worst-case 32-bit CNN workload, with only 41.3% area overhead.
ARCANE: Adaptive RISC-V Cache Architecture for Near-memory Extensions / Petrolo, V., Guella, F., Caon, M., Schiavone, P.D., Masera, G., Martina, M.. - (2025), pp. 1-6. (62nd ACM/IEEE Design Automation Conference, DAC 2025 usa 2025) [10.1109/dac63849.2025.11132598].
ARCANE: Adaptive RISC-V Cache Architecture for Near-memory Extensions
Petrolo, Vincenzo;Guella, Flavia;Caon, Michele;Masera, Guido;Martina, Maurizio
2025
Abstract
Modern data-driven applications expose limitations of von Neumann architectures-extensive data movement, low throughput, and poor energy efficiency. Accelerators improve performance but lack flexibility and require data transfers. Existing compute in- and near-memory solutions mitigate these issues but face usability challenges due to data placement constraints. We propose a novel cache architecture that doubles as a tightly-coupled compute-near-memory coprocessor. Our RISC-V cache controller executes custom instructions from the host CPU using vector operations dispatched to near-memory vector processing units within the cache memory subsystem. This architecture abstracts memory synchronization and data mapping from application software while offering software-based Instruction Set Architecture extensibility. Our implementation shows 30x to 84x performance improvement when operating on 8-bit data over the same system with a traditional cache when executing a worst-case 32-bit CNN workload, with only 41.3% area overhead.Pubblicazioni consigliate
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https://hdl.handle.net/11583/3015598
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