QUANTUM INSPIRED COMPUTING
The current trend in computation hardware focuses on developing custom accelerators to tackle the growing energy-efficiency and throughput demands of modern workloads such as Combinatorial Optimization Problems (COPs), Artificial Intelligence (AI), advanced communication systems, etc. While traditional digital architectures, including von Neumann-based processors and digital Application Specific Integrated Circuits (ASICs), constitute the main paradigm of conventional computing, they impose significant limitations. The inherent constraints of digital architectures stem from their reliance on clocked synchronous operation, restricted parallelism, and excessive data movement, leading to increased latency, high energy costs, and limited adaptability for specialized tasks. Recently, novel computing paradigms, such as quantum computing, have emerged as potential solutions to these challenges. Quantum computers use superposition, entanglement, and interference to process data in ways classical computers cannot. Quantum annealers, such as the D-Wave system [3], solve COPs by mapping them onto a quantum system's energy landscape. However, quantum computers and annealers face fundamental limitations that hinder their practical application.
Physics-inspired computers offer a promising alternative, emulating key features of quantum systems: i) direct mapping of compute loads to physical compute elements, ii) spin interactions that implement a dynamical system with an engineered energy landscape, iii) massive parallelism, iv) continuous-time (CT) asynchronous operation, and (v) flexibility in representing complex interactions, such as higher-order and non-linear couplings. CMOS-based solvers provide a practical realization of physics-inspired computing, by substituting quantum spin with the phase of oscillators, typically set at 0 and 180 degrees to represent binary 0 and 1. These solvers map complex optimization problems directly onto physical hardware, where interactions between oscillators define the problem’s objective function and energy landscape
E. Dikopoulos, Y.-T. Hsu*, L. Wormald*, W. Tang, Z. Zhang, M. P. Flynn “A Physics-Inspired Oscillator-based Mixed-Signal Optimization Engine for Solving 50-Variable 218- Clause 3-SAT Problems with 100% Solvability and 31.7 μs Solution Time” 2025 IEEE International Solid- State Circuits Conference (ISSCC).
E. Dikopoulos, L. Wormald, Y. -T. Hsu, W. Tang, P. -S. Chen, Z. Zhang, M. P. Flynn, “RXO-LDPC: A Physics-Inspired Relaxation Oscillator-Based Solver Leveraging 6-body Spin Interactions for Soft Decoding of LDPC Codes,” in IEEE Journal of Solid-State Circuits. In Press.
E. Dikopoulos, L. Wormald, Y.-T. Hsu, W. Tang, P.-S. Chen, Z. Zhang, M. P. Flynn, “A Relaxation Oscillator-Based Probabilistic Combinatorial Optimization Engine for Soft Decoding of LDPC Codes” European Solid-State Electronics Research Conference, September 2024.
E. Dikopoulos, L. Wormald, Y. -T. Hsu, W. Tang, P. -S. Chen, Z. Zhang, M. P. Flynn, “RXO-LDPC: A Physics-Inspired Relaxation Oscillator-Based Solver Leveraging 6-body Spin Interactions for Soft Decoding of LDPC Codes,” in IEEE Journal of Solid-State Circuits. In Press.
E. Dikopoulos, L. Wormald, Y.-T. Hsu, W. Tang, P.-S. Chen, Z. Zhang, M. P. Flynn, “A Relaxation Oscillator-Based Probabilistic Combinatorial Optimization Engine for Soft Decoding of LDPC Codes” European Solid-State Electronics Research Conference, September 2024.