Huazhong Yang

TL;DR: This paper presents an in-depth analysis of state-of-the-art CNN models and shows that Convolutional layers are computational-centric and Fully-Connected layers are memory-centric, and proposes a CNN accelerator design on embedded FPGA for Image-Net large-scale image classification.

TL;DR: The Efficient Speech Recognition Engine (ESE) as discussed by the authors proposes a load-balance-aware pruning method that can compress the LSTM model size by 20x (10x from pruning and 2x from quantization).

TL;DR: This paper proposes Angel-Eye, a programmable and flexible CNN accelerator architecture, together with data quantization strategy and compilation tool, which achieves similar performance and delivers up to better energy efficiency than peer FPGA implementation on the same platform.

TL;DR: This work proposes a load-balance-aware pruning method that can compress the LSTM model size by 20x (10x from pruning and 2x from quantization) with negligible loss of the prediction accuracy, and proposes a scheduler that encodes and partitions the compressed model to multiple PEs for parallelism and schedule the complicated L STM data flow.

TL;DR: In this article, the authors show that for typical IC packages and cooling structures, a given amount of heat introduced at any position in the active layer will have similar impact on the average temperature of the layer.