16 citations · 29 across the 14 of their papers we have counts for
6 papers · 1 filter
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…
Fault-Free Analog Computing with Imperfect Hardware
Zhicheng Xu, Jiawei Liu, Sitao Huang +9
The growing demand for edge computing and AI drives research into analog in-memory computing using memristors, which overcome data movement bottlenecks by computing directly within…
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…
Accelerating Hybrid XORCNF Boolean Satisfiability Problems Natively with In-Memory Computing
Haesol Im, Fabian Böhm, Giacomo Pedretti +14
The Boolean satisfiability (SAT) problem is a computationally challenging decision problem central to many industrial applications. For SAT problems in cryptanalysis, circuit desig…
Solving Boolean satisfiability problems with resistive content addressable memories
Giacomo Pedretti, Fabian Böhm, Tinish Bhattacharya +15
Solving optimization problems is a highly demanding workload requiring high-performance computing systems. Optimization solvers are usually difficult to parallelize in conventional…
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…