Trends and Challenges in Computing-in-Memory for Neural Network Model: A Review From Device Design to Application-Side Optimization
Neural network models have been widely used in various fields as the main way to solve
problems in the current artificial intelligence (AI) field. Efficient execution of neural network …
problems in the current artificial intelligence (AI) field. Efficient execution of neural network …
Design of a Mixed-Signal Compute-in-Memory Ising Solver With Sub-s Time-to-Solution and Optimal Decaying Noise Profile
AM Dee, D Vuong, K Bennett… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Combinatorial and discrete optimization problems are prevalent in fields such as artificial
intelligence, supply chain management, and wireless communications. The Ising machine, a …
intelligence, supply chain management, and wireless communications. The Ising machine, a …
FlexSpin: A CMOS Ising Machine With 256 Flexible Spin Processing Elements With 8-b Coefficients for Solving Combinatorial Optimization Problems
Combinatorial optimization problems (COPs) are essential in various applications, including
data clustering, supply chain management, and communication networks. Many real-world …
data clustering, supply chain management, and communication networks. Many real-world …
PRESTO: A Processing-in-Memory-Based -SAT Solver Using Recurrent Stochastic Neural Network With Unsupervised Learning
In this article, we introduce a processing-in-memory (PIM)-based satisfiability (SAT) solver
called Processing-in-memory-based SAT solver using a Recurrent Stochastic neural …
called Processing-in-memory-based SAT solver using a Recurrent Stochastic neural …