Architecture for memory-based computing: a case study on SNN-based database

The paper presents a novel flexible low-power architecture for memory-based computing that relies on a NoC and power-gated distributed MRAM. The proposed approach is demonstrated with a database search application implemented with a Sparse-Neural-Network (SNN). Multiple SystemC simulations have been conducted over the MRAM-based computing architecture targeting hundreds of database queries. The results show hit rates of about 95%, impressive power gains compared to SRAM, and significant impact of power-gating. The results also provide an evidence on the feasibility of using power-gated MRAM associated with a NoC as a solution for low power implementation of memory-based computing.      «

The paper presents a novel flexible low-power architecture for memory-based computing that relies on a NoC and power-gated distributed MRAM. The proposed approach is demonstrated with a database search application implemented with a Sparse-Neural-Network (SNN). Multiple SystemC simulations have been conducted over the MRAM-based computing architecture targeting hundreds of database queries. The results show hit rates of about 95%, impressive power gains compared to SRAM, and significant impact...     »