Researchers proposed an all-analog #chip for high-speed vision tasks. By applying diffractive optical computing, the light-induced photocurrents are directly used for further #calculation, increasing its systemic energy efficiency to 74.8 peta-operations per second per watt and #ComputingSpeed to 4.6 peta-operations per second. The chip can be widely used in wearable devices, autonomous driving, industrial inspections, and more fields. For more @Nature: go.nature.com/47fm6Vn

All-analog photoelectronic chip for high-speed vision tasks

Abstract：Photonic computing enables faster and more energy-efficient processing of vision data1,2,3,4,5. However, experimental superiority of deployable systems remains a challenge because of complicated optical nonlinearities, considerable power consumption of analog-to-digital converters (ADCs) for downstream digital processing and vulnerability to noises and system errors1,6,7,8. Here we propose an all-analog chip combining electronic and light computing (ACCEL). It has a systemic energy efficiency of 74.8 peta-operations per second per watt and a computing speed of 4.6 peta-operations per second (more than 99% implemented by optics), corresponding to more than three and one order of magnitude higher than state-of-the-art computing processors, respectively. After applying diffractive optical computing as an optical encoder for feature extraction, the light-induced photocurrents are directly used for further calculation in an integrated analog computing chip without the requirement of analog-to-digital converters, leading to a low computing latency of 72 ns for each frame. With joint optimizations of optoelectronic computing and adaptive training, ACCEL achieves competitive classification accuracies of 85.5%, 82.0% and 92.6%, respectively, for Fashion-MNIST, 3-class ImageNet classification and time-lapse video recognition task experimentally, while showing superior system robustness in low-light conditions (0.14 fJ μm−2 each frame). ACCEL can be used across a broad range of applications such as wearable devices, autonomous driving and industrial inspections.