133 citations · 302 across the 21 of their papers we have counts for
22 papers
TAP-CAM: A Tunable Approximate Matching Engine based on Ferroelectric Content Addressable Memory
Chenyu Ni, Sijie Chen, Che-Kai Liu +8
Pattern search is crucial in numerous analytic applications for retrieving data entries akin to the query. Content Addressable Memories (CAMs), an in-memory computing fabric, direc…
A Remedy to Compute-in-Memory with Dynamic Random Access Memory: 1FeFET-1C Technology for Neuro-Symbolic AI
Xunzhao Yin, Hamza Errahmouni Barkam, Franz Müller +14
Neuro-symbolic artificial intelligence (AI) excels at learning from noisy and generalized patterns, conducting logical inferences, and providing interpretable reasoning. Comprising…
Shared-PIM: Enabling Concurrent Computation and Data Flow for Faster Processing-in-DRAM
Ahmed Mamdouh, Haoran Geng, Michael Niemier +2
Processing-in-Memory (PIM) enhances memory with computational capabilities, potentially solving energy and latency issues associated with data transfer between memory and processor…
Secure Scattered Memory: Rethinking Secure Enclave Memory with Secret Sharing
Haoran Geng, Yuezhi Che, Dazhao Chen +2
The rise of cloud computing demands secure memory systems that ensure data confidentiality, integrity, and freshness against replay attacks. Existing schemes such as AES-XTS, AES-G…
Efficient approximation of Earth Mover's Distance Based on Nearest Neighbor Search
Guangyu Meng, Ruyu Zhou, Liu Liu +5
Earth Mover's Distance (EMD) is an important similarity measure between two distributions, used in computer vision and many other application domains. However, its exact calculatio…
Privacy Preserving In-memory Computing Engine
Haoran Geng, Jianqiao Mo, Dayane Reis +5
Privacy has rapidly become a major concern/design consideration. Homomorphic Encryption (HE) and Garbled Circuits (GC) are privacy-preserving techniques that support computations o…
Accelerating Polynomial Modular Multiplication with Crossbar-Based Compute-in-Memory
Mengyuan Li, Haoran Geng, Michael Niemier +1
Lattice-based cryptographic algorithms built on ring learning with error theory are gaining importance due to their potential for providing post-quantum security. However, these al…
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…
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…
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…
MIMHD: Accurate and Efficient Hyperdimensional Inference Using Multi-Bit In-Memory Computing
Arman Kazemi, Mohammad Mehdi Sharifi, Zhuowen Zou +3
Hyperdimensional Computing (HDC) is an emerging computational framework that mimics important brain functions by operating over high-dimensional vectors, called hypervectors (HVs).…
Application-driven Design Exploration for Dense Ferroelectric Embedded Non-volatile Memories
Mohammad Mehdi Sharifi, Lillian Pentecost, Ramin Rajaei +8
The memory wall bottleneck is a key challenge across many data-intensive applications. Multi-level FeFET-based embedded non-volatile memories are a promising solution for denser an…
Logic Compatible High-Performance Ferroelectric Transistor Memory
Sourav Dutta, Huacheng Ye, Abhishek Khanna +11
Silicon ferroelectric field-effect transistors (FeFETs) with low-k interfacial layer (IL) between ferroelectric gate stack and silicon channel suffers from high write voltage, limi…
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…
A Device Non-Ideality Resilient Approach for Mapping Neural Networks to Crossbar Arrays
Arman Kazemi, Cristobal Alessandri, Alan C. Seabaugh +3
We propose a technology-independent method, referred to as adjacent connection matrix (ACM), to efficiently map signed weight matrices to non-negative crossbar arrays. When compare…
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…
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…
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…