Design of Switched-Current Based Low-Power PIM Vision System for IoT Applications

Neural networks(NN) is becoming dominant in machine learning field for its excellent performance in classification, recognition and so on. However, the huge computation and memory overhead make it hard to implement NN algorithms on the existing platforms with real-time and energy-efficient performance. In this work, a low-power processing-in-memory (PIM) vision system for accelerate binary weight networks is proposed. This architecture utilizes PIM and features an energy-efficient switched current (SI) neuron, employing a network with binary weight and 9-bit activation. Simulation result shows the design occupies 5.82mm2 in SMIC 180nm CMOS technology, which consumes 1.45mW from 1.8V supplies. Our system outperforms the state-of-the-art designs in terms of power consumption and achieves energy efficiency up to 28.25TOPS/W.