mlsanityData to 14 Oct 2026

AI-Guided Codesign Framework for Novel Material and Device Design applied to MTJ-based True Random Number Generators

arXiv 2411.01008 ↗First posted 1 Nov 2024cs.ET cs.LG cs.NEcited by 1

Not on the map. Why?

Abstract (arXiv)

Novel devices and novel computing paradigms are key for energy efficient, performant future computing systems. However, designing devices for new applications is often time consuming and tedious. Here, we investigate the design and optimization of spin orbit torque and spin transfer torque magnetic tunnel junction models as the probabilistic devices for true random number generation. We leverage reinforcement learning and evolutionary optimization to vary key device and material properties of the various device models for stochastic operation. Our AI guided codesign methods generated different candidate devices capable of generating stochastic samples for a desired probability distribution, while also minimizing energy usage for the devices.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 8 of its 50 references that are in the map. First posted on arXiv 1 Nov 2024.

Builds on

8 of 50 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021Physics-informed neural networks with hard constraints for inverse designcited by 280
  3. 2021A Practical Guide to Multi-Objective Reinforcement Learning and Planningcited by 256
  4. 2018The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communitiescited by 78
  5. 2020AutoCkt: Deep Reinforcement Learning of Analog Circuit Designscited by 51
  6. 2021A Full-Stack Search Technique for Domain Optimized Deep Learning Acceleratorscited by 19
  7. 2021DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networkscited by 18
  8. 2023A full-stack view of probabilistic computing with p-bits: devices, architectures and algorithmscited by 13

Built on by

Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Correspondence Between Ising Machines and Neural Networkscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2023Probabilistic Compute-in-Memory Design For Efficient Markov Chain Monte Carlo Sampling ↗
  2. 2024Synergistic Development of Perovskite Memristors and Algorithms for Robust Analog Computing
  3. 2025AI Methods for Permutation Circuit Synthesis Across Generic Topologies
  4. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
  5. 2026A Unified Physics-Aware Quantum Machine Learning Framework across Power GaN HEMTs and Logic Nanowire FETs: Predicting Unseen Process Splits and Held-Out Geometry Combinations with Lower Error and Tighter Split-to-Split Variability
  6. 2021Implementation of a Binary Neural Network on a Passive Array of Magnetic Tunnel Junctions
  7. 2026AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL
  8. 2026AlphaCNOT: Learning CNOT Minimization with Model-Based Planning