An Analog-Memoryless Near Sensor Computing Architecture for Always-On Intelligent Perception Applications

In always-on intelligent visual perception applications, Convolution Neural Network algorithm has been widely applied. However, the implementation of it's processing is limited by hardware resources and energy budgets, which come from data access, interface overhead and inflexible configuration. In this paper, we present a novel analog architecture that implements low-precision Binary Neural Network and fuses with CMOS image sensor for always-on applications. By enabling current-mode computation in analog domain, the overhead of analog-to-digital converter as well as analog buffer is eliminated. And in order to relax the overhead of CIS, conventional correlating-double-sampling is moved to Computation Module. Moreover, the architecture is also dynamic-configurable, which is optimized specifically for CIS data flow. Simulations with HSPICE show that the peak energy efficiency of proposed architecture can achieve 13.91 TOPS/W at the 8-bit percision of multiplication-and-accumulation analog computation, while accuracy loss is only about 5.66%.