87 citations · 809 across the 93 of their papers we have counts for
5 papers · 1 filter
Computing-In-Memory Neural Network Accelerators for Safety-Critical Systems: Can Small Device Variations Be Disastrous?
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-Memory (CiM) architectures based on emerging non-volatile memory (NVM) devices have demonstrated great potential for deep neural network (DNN) acceleration thanks to t…
On the Reliability of Computing-in-Memory Accelerators for Deep Neural Networks
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-memory with emerging non-volatile memory (nvCiM) is shown to be a promising candidate for accelerating deep neural networks (DNNs) with high energy efficiency. However…
A Semi-Decoupled Approach to Fast and Optimal Hardware-Software Co-Design of Neural Accelerators
Bingqian Lu, Zheyu Yan, Yiyu Shi +1
In view of the performance limitations of fully-decoupled designs for neural architectures and accelerators, hardware-software co-design has been emerging to fully reap the benefit…
Uncertainty Modeling of Emerging Device-based Computing-in-Memory Neural Accelerators with Application to Neural Architecture Search
Zheyu Yan, Da-Cheng Juan, Xiaobo Sharon Hu +1
Emerging device-based Computing-in-memory (CiM) has been proved to be a promising candidate for high-energy efficiency deep neural network (DNN) computations. However, most emergin…
Design-Phase Buffer Allocation for Post-Silicon Clock Binning by Iterative Learning
Li Zhang, Bing Li, Jinglan Liu +2
At submicron manufacturing technology nodes, pro- cess variations affect circuit performance significantly. To counter these variations, engineers are reserving more timing margin…