CAREER: Designing Ultra-Energy-Efficient Intelligent Hardware with On-Chip Learning, Attention, and Inference

Arizona State University, Scottsdale AZ

Investigators

Abstract

Building intelligent computers that can perform cognitive tasks (e.g., learning, recognition) as well as humans do has been a long-standing goal of computing research. State-of-the-art deep learning and neuromorphic algorithms have recently advanced the software performance for cognitive applications. However, such algorithms are computation-memory-communication intensive, which makes the hardware design challenging to perform low-power real-time training and classification on portable platforms. Furthermore, to optimize system-level power, efficient power delivery and supply voltage regulation of such large-scale hardware systems also becomes a critical concern. This project will address these challenges across multiple disciplines of hardware and software design, towards the overarching goal of building brain-inspired intelligent computing systems that are ultra-energy-efficient for various cognitive tasks in computer vision, speech, robotics and biomedical applications. The success of this research is likely to impact many user-centric computing systems in society and industry, including wearable, mobile, and edge computing. This project also entails integrative education and outreach plans through a new interdisciplinary coursework development, undergraduate/graduate student training, and a summer outreach program for high school students. In this project, energy-efficient circuits, architectures and algorithms will be designed to incorporate learning, attention and inference computations in area-/power-constrained mobile/wearable hardware platforms. The particular technologies that will be developed to achieve large improvement in energy-efficiency include: (1) computation redundancy minimization of state-of-the-art deep learning algorithms with bio-inspired attention models, (2) novel memory compression schemes that apply to both software and hardware implementation, (3) real-time on-chip learning methods that consume low power on mobile/wearable devices, (4) efficient on-chip voltage regulators that can adapt to abrupt changes in cognitive workloads, and (5) cross-layer optimization of circuit, architecture and algorithm. The outcomes of this research will feature new very-large-scale integration (VLSI) systems that can learn and perform cognitive tasks in real-time with superior power efficiency, opening up possibilities for ubiquitous intelligence in small-form-factor devices.