Millimeter-Scale Ultra-Low-Power Imaging System for Intelligent Edge Monitoring

Millimeter-scale embedded sensing systems have unique advantages over larger devices as they are able to capture, analyze, store, and transmit data at the source while being unobtrusive and covert. However, area-constrained systems pose several challenges, including a tight energy budget and peak power, limited data storage, costly wireless communication, and physical integration at a miniature scale. This paper proposes a novel 6.7$\times$7$\times$5mm imaging system with deep-learning and image processing capabilities for intelligent edge applications, and is demonstrated in a home-surveillance scenario. The system is implemented by vertically stacking custom ultra-low-power (ULP) ICs and uses techniques such as dynamic behavior-specific power management, hierarchical event detection, and a combination of data compression methods. It demonstrates a new image-correcting neural network that compensates for non-idealities caused by a mm-scale lens and ULP front-end. The system can store 74 frames or offload data wirelessly, consuming 49.6$\mu$W on average for an expected battery lifetime of 7 days.

Neural Cache: Bit-Serial In-Cache Acceleration of Deep Neural Networks

This paper presents the Neural Cache architecture, which re-purposes cache structures to transform them into massively parallel compute units capable of running inferences for Deep Neural Networks. Techniques to do in-situ arithmetic in SRAM arrays, create efficient data mapping and reducing data movement are proposed. The Neural Cache architecture is capable of fully executing convolutional, fully connected, and pooling layers in-cache. The proposed architecture also supports quantization in-cache. Our experimental results show that the proposed architecture can improve inference latency by 18.3x over state-of-art multi-core CPU (Xeon E5), 7.7x over server class GPU (Titan Xp), for Inception v3 model. Neural Cache improves inference throughput by 12.4x over CPU (2.2x over GPU), while reducing power consumption by 50% over CPU (53% over GPU).