accelerators

Hardware Centric Quantized Convolutional Neural Network and Algorithms

Li Zhang, Ph.D. Student, Electrical and Computer Engineering

Jul 24, 09:00 - 10:00

B3 L5 R5209

machine learning accelerators FPGA

This thesis addresses the challenges of deploying quantized convolutional neural networks (QCNNs) on resource-constrained edge devices by proposing two novel hardware-software co-design frameworks: one for deriving lightweight, hardware-friendly models validated on FPGA, and another for hardware-aware mixed-precision quantization on compute-in-memory accelerators.