Abstract
In the intelligent IoT edge devices, power consumption is increasing due to the deployment of high-precision algorithms, which greatly limits the working time of the devices. The power of A/D conversion and data transmission has become the bottleneck of traditional visual system. In this paper, a new sensing-with-computing (Senputing) architecture is proposed to reduce this power bottleneck by combining imaging and BNN 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> -layer feature map computation. This Senputing architecture has two working modes, Normal-Sensor mode and Direct-Photocurrent-Computation mode with different resolutions (128×128 and 32×32). An ultra-low-power CMOS image sensor (CIS) chip with Senputing architecture is proposed to verify the feasibility. Our CIS chip is simulated with 180nm CMOS technology, the power of feature map computation is 5.9μW, and the frame rate is 208fps. The computation efficiency reaches to 8.23TOPs/W, which is 10.1 x higher than previous works.
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