12 citations · 28 across the 3 of their papers we have counts for
6 papers
Standing on the Shoulders of Giants: Hardware and Neural Architecture Co-Search with Hot Start
Weiwen Jiang, Lei Yang, Sakyasingha Dasgupta +2
Hardware and neural architecture co-search that automatically generates Artificial Intelligence (AI) solutions from a given dataset is promising to promote AI democratization; howe…
Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks
Lei Yang, Zheyu Yan, Meng Li +6
Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs).…
Device-Circuit-Architecture Co-Exploration for Computing-in-Memory Neural Accelerators
Weiwen Jiang, Qiuwen Lou, Zheyu Yan +4
Co-exploration of neural architectures and hardware design is promising to simultaneously optimize network accuracy and hardware efficiency. However, state-of-the-art neural archit…
Achieving Super-Linear Speedup across Multi-FPGA for Real-Time DNN Inference
Weiwen Jiang, Edwin H. -M. Sha, Xinyi Zhang +4
Real-time Deep Neural Network (DNN) inference with low-latency requirement has become increasingly important for numerous applications in both cloud computing (e.g., Apple's Siri)…
Hardware/Software Co-Exploration of Neural Architectures
Weiwen Jiang, Lei Yang, Edwin Sha +5
We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed h…
Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search
Weiwen Jiang, Xinyi Zhang, Edwin H. -M. Sha +4
A fundamental question lies in almost every application of deep neural networks: what is the optimal neural architecture given a specific dataset? Recently, several Neural Architec…