133 citations · 473 across the 24 of their papers we have counts for
26 papers
COSIME: FeFET based Associative Memory for In-Memory Cosine Similarity Search
Che-Kai Liu, Haobang Chen, Mohsen Imani +8
In a number of machine learning models, an input query is searched across the trained class vectors to find the closest feature class vector in cosine similarity metric. However, p…
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…
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…
SWIM: Selective Write-Verify for Computing-in-Memory Neural Accelerators
Zheyu Yan, Xiaobo Sharon Hu, Yiyu Shi
Computing-in-Memory architectures based on non-volatile emerging memories have demonstrated great potential for deep neural network (DNN) acceleration thanks to their high energy e…
IMCRYPTO: An In-Memory Computing Fabric for AES Encryption and Decryption
Dayane Reis, Haoran Geng, Michael Niemier +1
This paper proposes IMCRYPTO, an in-memory computing (IMC) fabric for accelerating AES encryption and decryption. IMCRYPTO employs a unified structure to implement encryption and d…
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…
Computing-in-Memory for Performance and Energy Efficient Homomorphic Encryption
Dayane Reis, Jonathan Takeshita, Taeho Jung +2
Homomorphic encryption (HE) allows direct computations on encrypted data. Despite numerous research efforts, the practicality of HE schemes remains to be demonstrated. In this rega…
FeCAM: A Universal Compact Digital and Analog Content Addressable Memory Using Ferroelectric
Xunzhao Yin, Chao Li, Qingrong Huang +5
Ferroelectric field effect transistors (FeFETs) are being actively investigated with the potential for in-memory computing (IMC) over other non-volatile memories (NVMs). Content Ad…
A Hybrid FeMFET-CMOS Analog Synapse Circuit for Neural Network Training and Inference
Arman Kazemi, Ramin Rajaei, Kai Ni +3
An analog synapse circuit based on ferroelectric-metal field-effect transistors is proposed, that offers 6-bit weight precision. The circuit is comprised of volatile least signific…
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…
Nonvolatile Spintronic Memory Cells for Neural Networks
Andrew W. Stephan, Qiuwen Lou, Michael Niemier +2
A new spintronic nonvolatile memory cell analogous to 1T DRAM with non-destructive read is proposed. The cells can be used as neural computing units. A dual-circuit neural network…
Application-level Studies of Cellular Neural Network-based Hardware Accelerators
Qiuwen Lou, Indranil Palit, Tang Li +3
As cost and performance benefits associated with Moore's Law scaling slow, researchers are studying alternative architectures (e.g., based on analog and/or spiking circuits) and/or…
Fully Distributed Packet Scheduling Framework for Handling Disturbances in Lossy Real-Time Wireless Networks
Tianyu Zhang, Tao Gong, Song Han +2
Along with the rapid growth of Industrial Internet-of-Things (IIoT) applications and their penetration into many industry sectors, real-time wireless networks (RTWNs) have been pla…
Eva-CiM: A System-Level Performance and Energy Evaluation Framework for Computing-in-Memory Architectures
Di Gao, Dayane Reis, Xiaobo Sharon Hu +1
Computing-in-Memory (CiM) architectures aim to reduce costly data transfers by performing arithmetic and logic operations in memory and hence relieve the pressure due to the memory…
CC-Net: Image Complexity Guided Network Compression for Biomedical Image Segmentation
Suraj Mishra, Peixian Liang, Adam Czajka +2
Convolutional neural networks (CNNs) for biomedical image analysis are often of very large size, resulting in high memory requirement and high latency of operations. Searching for…
A mixed signal architecture for convolutional neural networks
Qiuwen Lou, Chenyun Pan, John McGuiness +4
Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems. Convolutio…
DAC-SDC Low Power Object Detection Challenge for UAV Applications
Xiaowei Xu, Xinyi Zhang, Bei Yu +4
The 55th Design Automation Conference (DAC) held its first System Design Contest (SDC) in 2018. SDC'18 features a lower power object detection challenge (LPODC) on designing and im…
Quantization of Fully Convolutional Networks for Accurate Biomedical Image Segmentation
Xiaowei Xu, Qing Lu, Yu Hu +4
With pervasive applications of medical imaging in health-care, biomedical image segmentation plays a central role in quantitative analysis, clinical diagno- sis, and medical interv…
Optimizing Memory Efficiency for Convolution Kernels on Kepler GPUs
Xiaoming Chen, Jianxu Chen, Danny Z. Chen +1
Convolution is a fundamental operation in many applications, such as computer vision, natural language processing, image processing, etc. Recent successes of convolutional neural n…
Efficient Analog Circuits for Boolean Satisfiability
Xunzhao Yin, Behnam Sedighi, Melinda Varga +3
Efficient solutions to NP-complete problems would significantly benefit both science and industry. However, such problems are intractable on digital computers based on the von Neum…