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.