Efficient Algorithms for In-Memory Fixed Point Multiplication Using MAGIC

The growing disparity between processor and memory performance poses significant limits on system performance and energy efficiency. To address this widely investigated problem, modern computing systems attempt to minimize data transfer by means of a memory hierarchy. Yet the benefit from such a solution for data-intensive applications is limited. Emerging non-volatile resistive memory technologies (memristors) offer the ability to both store and process data within the memristive memory cells, with almost no data transfer. In this paper, we propose algorithms for performing fixed point multiplication within the memristive memory using Memristor Aided Logic (MAGIC) gates and execute them in a cycle-accurate simulator to verify and evaluate them. Previously proposed implementations were not feasible for execution within memory because the required number of memory cells for the computation was too large to fit the size-limited memristive memory arrays. The algorithms proposed in this paper not only improve the latency as compared to previously proposed algorithms by 1.8 on average, but their significantly better area efficiency now makes it possible to perform numerous fixed point multiplications simultaneously within memristive memory arrays