22 citations · 41 across the 7 of their papers we have counts for
7 papers
Supporting Higher-Order Interactions in Practical Ising Machines
Nafisa Sadaf Prova, Hüsrev Cılasun, Abhimanyu Kumar +3
Ising machines as hardware solvers of combinatorial optimization problems (COPs) can efficiently explore large solution spaces due to their inherent parallelism and physics-based d…
HETRI: Heterogeneous Ising Multiprocessing
Ahmet Efe, Hüsrev Cılasun, Abhimanyu Kumar +4
Ising machines are effective solvers for complex combinatorial optimization problems. The idea is mapping the optimal solution(s) to a combinatorial optimization problem to the min…
Experimental demonstration of magnetic tunnel junction-based computational random-access memory
Yang Lv, Brandon R. Zink, Robert P. Bloom +9
Conventional computing paradigm struggles to fulfill the rapidly growing demands from emerging applications, especially those for machine intelligence, because much of the power an…
3SAT on an All-to-All-Connected CMOS Ising Solver Chip
Hüsrev Cılasun, Ziqing Zeng, Ramprasath S +6
This work solves 3SAT, a classical NP-complete problem, on a CMOS-based Ising hardware chip with all-to-all connectivity. The paper addresses practical issues in going from algorit…
On Error Correction for Nonvolatile Processing-In-Memory
Hüsrev Cılasun, Salonik Resch, Zamshed I. Chowdhury +6
Processing in memory (PiM) represents a promising computing paradigm to enhance performance of numerous data-intensive applications. Variants performing computing directly in emerg…
Exploring the Feasibility of Using 3D XPoint as an In-Memory Computing Accelerator
Masoud Zabihi, Salonik Resch, Husrev Cılasun +5
This paper describes how 3D XPoint memory arrays can be used as in-memory computing accelerators. We first show that thresholded matrix-vector multiplication (TMVM), the fundamenta…
An Inference and Learning Engine for Spiking Neural Networks in Computational RAM (CRAM)
Hüsrev Cılasun, Salonik Resch, Zamshed I. Chowdhury +8
Spiking Neural Networks (SNN) represent a biologically inspired computation model capable of emulating neural computation in human brain and brain-like structures. The main promise…