An Ultra-low Power TinyML System for Real-time Visual Processing at Edge

Tiny machine learning (TinyML), executing AI workloads on resource and power strictly restricted systems, is an important and challenging topic. This brief firstly presents an extremely tiny backbone to construct high efficiency CNN models for various visual tasks. Then, a specially designed neural co-processor (NCP) is interconnected with MCU to build an ultra-low power TinyML system, which stores all features and weights on chip and completely removes both of latency and power consumption in off-chip memory access. Furthermore, an application specific instruction-set is further presented for realizing agile development and rapid deployment. Extensive experiments demonstrate that the proposed TinyML system based on our model, NCP and instruction set yields considerable accuracy and achieves a record ultra-low power of 160mW while implementing object detection and recognition at 30FPS. The demo video is available on \url{https://www.youtube.com/watch?v=mIZPxtJ-9EY}.Comment: 5 pages, 5 figure

Convolutional Neural Network Defect Detection Algorithm for Wire Bonding X-ray Images

To address the challenges of complex backgrounds, small defect sizes, and diverse defect types in defect detection of wire bonding X-ray images, this paper proposes a convolutional-neural-network-based defect detection method called YOLO-CSS. This method designs a novel feature extraction network that effectively captures semantic features from different gradient information. It utilizes a self-adaptive weighted multi-scale feature fusion module called SMA which adaptively weights the contribution of detection results based on different scales of feature maps. Simultaneously, skip connections are employed at the bottleneck of the network to ensure the integrity of feature information. Experimental results demonstrate that on the wire bonding X-ray defect image dataset, the proposed algorithm achieves mAP 0.5 and mAP 0.5–0.95 values of 97.3% and 72.1%, respectively, surpassing the YOLO series algorithms. It also exhibits certain advantages in terms of model size and detection speed, effectively balancing detection accuracy and speed

Wearable LIG Flexible Stress Sensor Based on Spider Web Bionic Structure

Bionic structures are widely used in scientific research. Through the observation and study of natural biological structure, it is found that spider web structure is composed of many radial silk lines protruding from the center and spiral silk lines surrounding the center. It has high stability and high sensitivity, and is especially suitable for the production of sensors. In this study, a flexible graphene sensor based on a spider web bionic structure is reported. Graphene, with its excellent mechanical properties and high electrical conductivity, is an ideal material for making sensors. In this paper, laser-induced graphene (LIG) is used as a sensing material to make a spider web structure, which is encapsulated onto a polydimethylsiloxane (PDMS) substrate to make a spider web structured graphene flexible strain sensor. The study found that the stress generated by the sensor of the spider web structure in the process of stretching and torsion can be evenly distributed in the spider web structure, which has excellent resonance ability, and the overall structure shows good structural robustness. In the experimental test, it is shown that the flexible stress sensor with spider web structure achieves high sensitivity (GF is 36.8), wide working range (0–35%), low hysteresis (260 ms), high repeatability and stability, and has long-term durability. In addition, the manufacturing process of the whole sensor is simple and convenient, and the manufactured sensor is economical and durable. It shows excellent stability in finger flexion and extension, fist clenching, and arm flexion and extension applications. This shows that the sensor can be widely used in wearable sensing devices and the detection of human biological signals. Finally, it has certain development potential in the practical application of medical health, motion detection, human-computer interaction and other fields