ARCANE: Adaptive RISC-V Cache Architecture for Near-memory Extensions
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 nearmemory 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 30× to 84× 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.
École Polytechnique Fédérale de Lausanne
2025-06-22
979-8-3315-0304-8
1
6
REVIEWED
EPFL
| Event name | Event acronym | Event place | Event date |
DAC 2025 | San Francisco, CA, USA | 2025-06-22 - 2025-06-25 | |