Ning Liu

TL;DR: The CirCNN architecture is proposed, a universal DNN inference engine that can be implemented in various hardware/software platforms with configurable network architecture (e.g., layer type, size, scales, etc) and FFT can be used as the key computing kernel which ensures universal and small-footprint implementations.

TL;DR: The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem and the proposed framework can achieve the best trade-off between latency and power/energy consumption in a server cluster.

TL;DR: This work proposes AutoCompress, an automatic structured pruning framework with the following key performance improvements: effectively incorporate the combination of structured pruned schemes in the automatic process, and adopt the state-of-art ADMM-based structured weight pruning as the core algorithm.

TL;DR: CirCNN as discussed by the authors utilizes the Fast Fourier Transform (FFT)-based fast multiplication, simultaneously reducing the computational complexity (both in inference and training) and the storage complexity from O(n2) to O(nlogn) with negligible accuracy loss.

TL;DR: VIBNN as mentioned in this paper is an FPGA-based hardware accelerator design for variational inference on BNNs, which can achieve throughput of 321,543.4 Images/s and energy efficiency upto 52,694.8 Images/J.