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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.
