MASIM: An Efficient Multi-Array Scheduler for In-Memory SIMD Computation

TL;DR

MASIM is a novel multi-array scheduler for in-memory SIMD computation that significantly reduces energy consumption by minimizing copy instructions through a priority-driven algorithm and iterative improvements.

Contribution

It introduces MASIM, a new scheduler that effectively reduces copy instructions and energy use in in-memory SIMD architectures, outperforming existing schedulers.

Findings

Reduces copy instructions by 63.2% on average.

Achieves 28.0% reduction in energy consumption.

Outperforms state-of-the-art schedulers in efficiency.

Abstract

Single instruction, multiple data (SIMD) is a popular design style of in-memory computing (IMC) architectures, which enables memory arrays to perform logic operations to achieve low energy consumption and high parallelism. To implement a target function on the data stored in memory, the function is first transformed into a netlist of the supported logic operations through logic synthesis. Then, the scheduler transforms the netlist into the instruction sequence given to the architecture. An instruction is either computing a logic operation in the netlist or copying the data from one array to another. Most existing schedulers focus on optimizing the execution sequence of the operations to minimize the number of memory rows needed, neglecting the energy-consuming copy instructions, which cannot be avoided when working with arrays with limited sizes. In this work, our goal is to reduce the number of copy instructions to decrease overall energy consumption. We propose MASIM, a multi-array scheduler for in-memory SIMD computation. It consists of a priority-driven scheduling algorithm and an iterative improvement process. Compared to the best state-of-the-art scheduler, MASIM reduces the number of copy instructions by 63.2% on average, which leads to a 28.0% reduction in energy.