11 citations · 13 across the 6 of their papers we have counts for
12 papers
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…
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…
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…