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Algorithm-Hardware Co-design for Low-Power Smart Home AI Devices

thesis
posted on 01.05.2020, 00:00 by Nikolai Iliev
Recent advances in CMOS VLSI technology have enabled the tremendous growth of devices at the edge of the cloud and in indoor environments: IoT indoor appliances, mobile indoor medical assistants, mobile indoor manufacturing platforms, indoor drone assistants, and others. As anticipated, this growth (in edge-device numbers and capabilities) is generating large communications and data processing workloads for the servers in the cloud. One approach to help manage this trend is to make the edge-nodes more intelligent and able to process more data onboard (within the edge-node) before communicating with the servers. This thesis proposes hardware accelerator solutions to three types of onboard (within platform) processing: Spatial Self-Localization (SSL) which localizes the platform in space, Speaker Recognition (SpkrRec) which allows human voice control of the platform and authentication of the human speaker, and Fully-Connected layer evaluation in Neural Networks ( FC-NN ) for accelerated neural network processing withing the platform. Onboard processing is assumed to include a multi-core SoC (CPU/GPU), conventional SRAM and DRAM memory as well as high bandwidth memory, HBM or 3D-DRAM, and communication and sensing subsystems. The SSL, SpkRec, and FC-NN accelerators can be integrated with the SoC’s peripheral bus structures such as AXI-Stream, AXI-Lite, AXI-HBM, JESD235A, JESD235B, GPMC, DMA, and similar high-speed processor interfaces.

History

Advisor

Trivedi, Amit R

Chair

Trivedi, Amit R

Department

Electic and Computer Engineering

Degree Grantor

University of Illinois at Chicago

Degree Level

Doctoral

Degree name

PhD, Doctor of Philosophy

Committee Member

Paprotny, Igor Rao, Wnjuung Metlushko, Vitali Zhang, Zhao Subramanian, Arun

Submitted date

May 2020

Thesis type

application/pdf

Language

en

Exports