1 citations · 2 across the 4 of their papers we have counts for
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Experimentally realized memristive memory augmented neural network
Ruibin Mao, Bo Wen, Yahui Zhao +8
Lifelong on-device learning is a key challenge for machine intelligence, and this requires learning from few, often single, samples. Memory augmented neural network has been propos…
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).…
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…
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…
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…