Invited — A 2.2 GHz SRAM with high temperature variation immunity for deep learning application under 28nm

With the coming era of Big Data, hardware implementation of machine learning has become attractive for many applications, such as real-time object recognition and face recognition. The implementation of machine learning algorithms needs intensive memory access, and SRAM is critical for the overall performance. This paper proposes a new design of high speed SRAM for machine learning purposes. With fast access time (cycle time: 650 ps, access time: 350 ps), low sensitivity to temperature variation and high configurability (less than 10% performance difference between 125_rcw_tt vs 0_rcw_tt), the proposed SRAM is a better candidate for hardware machine learning system than the conventional SRAM. Compared with Samsung HL 152, our design has smaller size (121×43 um2 vs 127×44 um2) with half the number of pins ports (12 vs 25) and higher speed (2.2GHz vs 0.8GHz).