In-memory computing (IMC) aims at solving the performance gap between CPU and memories introduced by the memory wall. However, general-purpose IMC does not consider the optimization of data transfers for patterns, such as stencils and convolutions. This article proposes a new instruction set architecture (ISA) and a novel pattern encoding for IMC to transfer and organize data streams in order to perform efficiently computation. This instruction set is implemented on the data-locality management unit (DMU) as a subset of the computational SRAM (C-SRAM) ISA. A programming model to interact with the DMU at language level is also presented in this article. This DMU ISA is evaluated on six applications run on three different system nodes. These system nodes are based on existing RISC-V cores and range from embedded to high-performance computing domain. Experiments show on average a speed-up of× 8.81, an energy reduction factor of× 6.81, and an improvement of the number of operations per cycle of× 4.59, for the C-SRAM architecture integrating the proposed ISA of the DMU compared to a reference implementation on embedded systems. Results also show an improvement of the number of operations per cycle of× 2.99compared to a reference implementation on all system nodes.
No takes yet. Share an insight, caveat, or question.
Mambu et al. (2023) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: