87 citations · 456 across the 39 of their papers we have counts for
5 papers · 1 filter
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…
On Neural Architecture Search for Resource-Constrained Hardware Platforms
Qing Lu, Weiwen Jiang, Xiaowei Xu +2
In the recent past, the success of Neural Architecture Search (NAS) has enabled researchers to broadly explore the design space using learning-based methods. Apart from finding bet…
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…