7 citations · 9 across the 9 of their papers we have counts for
9 papers
OTTER - Two Transistor - One RRAM Architecture for Reliable In-Memory-Computing in 28 nm CMOS Technology
Yang Chen, Daniele Storelli, Xinyi Zhao +20
This work presents OTTER, a 28 nm CMOS platform co-integrated with TaOx-based valence-change mechanism (VCM) RRAM, demonstrating a two-transistor-one-memristive-device (2T1R) archi…
A Fast and Energy-Efficient Latch-Based Memristive Analog Content-Addressable Memory
Paul-Philipp Manea, Aishwarya Natarajan, Jim Ignowski +2
Analog content-addressable memories (aCAMs) based on memristors provide a promising pathway toward energy-efficient large-scale associative computing for Edge AI and embedded intel…
QS4D: Quantization-aware training for efficient hardware deployment of structured state-space sequential models
Sebastian Siegel, Ming-Jay Yang, Younes Bouhadjar +3
Structured State Space models (SSM) have recently emerged as a new class of deep learning models, particularly well-suited for processing long sequences. Their constant memory foot…
Real-time raw signal genomic analysis using fully integrated memristor hardware
Peiyi He, Shengbo Wang, Ruibin Mao +6
Advances in third-generation sequencing have enabled portable and real-time genomic sequencing, but real-time data processing remains a bottleneck, hampering on-site genomic analys…
IMSSA: Deploying modern state-space models on memristive in-memory compute hardware
Sebastian Siegel, Ming-Jay Yang, John-Paul Strachan
Processing long temporal sequences is a key challenge in deep learning. In recent years, Transformers have become state-of-the-art for this task, but suffer from excessive memory r…
Gain Cell-Based Analog Content Addressable Memory for Dynamic Associative tasks in AI
Paul-Philipp Manea, Nathan Leroux, Emre Neftci +1
Analog Content Addressable Memories (aCAMs) have proven useful for associative in-memory computing applications like Decision Trees, Finite State Machines, and Hyper-dimensional Co…
Analog In-Memory Computing Attention Mechanism for Fast and Energy-Efficient Large Language Models
Nathan Leroux, Paul-Philipp Manea, Chirag Sudarshan +4
Transformer networks, driven by self-attention, are central to Large Language Models. In generative Transformers, self-attention uses cache memory to store token projections, avoid…
The Ouroboros of Memristors: Neural Networks Facilitating Memristor Programming
Zhenming Yu, Ming-Jay Yang, Jan Finkbeiner +3
Memristive devices hold promise to improve the scale and efficiency of machine learning and neuromorphic hardware, thanks to their compact size, low power consumption, and the abil…
Computing High-Degree Polynomial Gradients in Memory
T. Bhattacharya, G. H. Hutchinson, G. Pedretti +6
Specialized function gradient computing hardware could greatly improve the performance of state-of-the-art optimization algorithms, e.g., based on gradient descent or conjugate gra…