49 citations · 125 across the 28 of their papers we have counts for
16 papers · 1 filter
PRIM: Energy- and Endurance-Aware Compiler Optimization for Digital Compute in Non-volatile Memory
Patrick Miller, Ulya R. Karpuzcu
Compute-in-Memory (CiM) is an alternative computing paradigm that circumvents the memory bandwidth bottleneck. Variants utilizing non-volatile memory cells (NVCiM) to perform Boole…
Ising Acceleration for Multi-Robot Multi-Target Planning
Ahmet Efe, Recep B. Uludag, Chris H. Kim +1
Ising machines are emerging as promising hardware for combinatorial optimization. With recent advances in CMOS Ising technology, they are becoming attractive as low-power accelerat…
Breaking Local-Minimum Traps in Spiking Neural Network-Based Solvers for CSPs via Parallel Tempering
Recep Bugra Uludag, Ahmet Efe, Ismail Akturk +1
Spiking neural networks (SNNs) with stochastic neurons can solve constraint satisfaction problems (CSPs) by encoding constraints via connectivity and performing probabilistic searc…
Computing In Spintronic Memory: A Thermal Perspective
Patrick Miller, Hüsrev Cilasun, Sachin S. Sapatnekar +1
Computing-in-Memory (CiM) is a promising paradigm to address the memory bottleneck constraining traditional systems. Most power-efficient CiM variants can directly perform Boolean…
Implicitly Parallel Neuromorphic Solver Design for Constraint Satisfaction Problems
Recep Bugra Uludag, Ahmet Efe, Ismail Akturk +1
Many real-life problems of practical importance -- spanning a wide range of applications from chip design to bioinformatics -- represent constraint satisfaction problems, where cla…
An FPGA-ASIC Co-Design Framework for Capacity-Constrained Physics-Based Ising Chips
Ruihong Yin, Yue Zheng, Chaohui Li +6
When a problem exceeds an analog Ising machine's spin capacity it cannot be solved in one shot: a digital orchestration layer must iteratively decompose the graph, clamp boundary s…
On the Limits of Ising Machines for Circuit-Derived SAT
Ahmet Efe, Hüsrev Cılasun, Abhimanyu Kumar +9
Emerging Ising machines are promising for solving computationally hard optimization problems, yet their limits on structured, circuit-derived satisfiability (SAT) problems remain p…
DROID: Discrete-Time Simulation for Ring-Oscillator-Based Ising Design
Abhimanyu Kumar, Ramprasath S., Chris H. Kim +2
Many combinatorial problems can be mapped to Ising machines, i.e., networks of coupled oscillators that settle to a minimum-energy ground state, from which the problem solution is…
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…
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…
A Machine Learning Accelerator In-Memory for Energy Harvesting
Salonik Resch, S. Karen Khatamifard, Zamshed Iqbal Chowdhury +5
There is increasing demand to bring machine learning capabilities to low power devices. By integrating the computational power of machine learning with the deployment capabilities…
PIMBALL: Binary Neural Networks in Spintronic Memory
Salonik Resch, S. Karen Khatamifard, Zamshed Iqbal Chowdhury +5
Neural networks span a wide range of applications of industrial and commercial significance. Binary neural networks (BNN) are particularly effective in trading accuracy for perform…
On Memory System Design for Stochastic Computing
S. Karen Khatamifard, M. Hassan Najafi, Ali Ghoreyshi +2
Growing uncertainty in design parameters (and therefore, in design functionality) renders stochastic computing particularly promising, which represents and processes data as quanti…