AIM: Annealing in Memory for Vision Applications

Abstract

As the Moore’s law era will draw to a close, some domain-specific architectures even non-Von Neumann systems have been presented to keep the progress. This paper proposes novel annealing in memory (AIM) architecture to implement Ising calculation, which is based on Ising model and expected to accelerate solving combinatorial optimization problem. The Ising model has a symmetrical structure and realizes phase transition by symmetry breaking. AIM draws annealing calculation into memory to reduce the cost of information transfer between calculation unit and the memory, improves the ability of parallel processing by enabling each Static Random-Access Memory (SRAM) array to perform calculations. An approximate probability flipping circuit is proposed to avoid the system getting trapped in local optimum. Bit-serial design incurs only an estimated 4.24% area above the SRAM and allows the accuracy to be easily adjusted. Two vision applications are mapped for acceleration and results show that it can speed up Multi-Object Tracking (MOT) by 780× and Multiple People Head Detection (MPHD) by 161× with only 0.0064% and 0.031% energy consumption respectively over approximate algorithms.