16 citations · 29 across the 8 of their papers we have counts for
19 papers
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…
iMARS: An In-Memory-Computing Architecture for Recommendation Systems
Mengyuan Li, Ann Franchesca Laguna, Dayane Reis +3
Recommendation systems (RecSys) suggest items to users by predicting their preferences based on historical data. Typical RecSys handle large embedding tables and many embedding tab…
Deep Random Forest with Ferroelectric Analog Content Addressable Memory
Xunzhao Yin, Franz Müller, Ann Franchesca Laguna +14
Deep random forest (DRF), which incorporates the core features of deep learning and random forest (RF), exhibits comparable classification accuracy, interpretability, and low memor…
RADARS: Memory Efficient Reinforcement Learning Aided Differentiable Neural Architecture Search
Zheyu Yan, Weiwen Jiang, Xiaobo Sharon Hu +1
Differentiable neural architecture search (DNAS) is known for its capacity in the automatic generation of superior neural networks. However, DNAS based methods suffer from memory u…
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…
In-Memory Nearest Neighbor Search with FeFET Multi-Bit Content-Addressable Memories
Arman Kazemi, Mohammad Mehdi Sharifi, Ann Franchesca Laguna +6
Nearest neighbor (NN) search is an essential operation in many applications, such as one/few-shot learning and image classification. As such, fast and low-energy hardware support f…