An STT-MRAM Based in Memory Architecture for Low Power Integral Computing

Abstract

The integral histogram image plays an important role in accelerating the feature computation in vision algorithms. However, the computational process of the integral histogram, called integral computation, has high computational complexity and numerous memory access operations, which limit its wide application. This brief proposes an in-memory computational architecture based on Spin Transfer Torque Magnetic Random Access Memory (STT-MRAM) to solve these problems. The architecture can work in two different modes depending on the requirements: the integral computation mode and the memory mode. The architecture can figure out the integral histogram when in the integral computation mode, and just store the data directly when in the memory mode. Utilizing the non-volatile, high density and low power characteristics of STT-MRAM, we integrate the computational units into the memory array to achieve parallel computation. Reduced number of data transmission between storage units and computation units contributes to cut down the latency and energy consumption. The evaluation results show that, comparing with the state-of-the-art work, our architecture provides <inline-formula><tex-math notation="LaTeX">$1.1\times \sim 9\times$</tex-math></inline-formula> performance improvements and reduces 87.4 <inline-formula><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 97.3 percent energy consumption for <inline-formula><tex-math notation="LaTeX">$64\times 64\sim 512\times 512$</tex-math></inline-formula> size images, just with a 8 percent area overhead.