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IEEE International Conference on Quantum Computing and Engineering (QCE)

Engineering Discrete Simulated Bifurcation for an FPGA Digital Ising Machine

Fabrizio Orlando, Deborah Volpe, Giacomo Orlandi, Fabrizio Rientc, Marco Vacca, Mariagrazia Graziano

Published Sep 15, 2024• 1 citation
Publisher

Abstract

Simulated Adiabatic Bifurcation (aSB) is a quantum-inspired algorithm that provides approximate solutions for large-scale optimization problems using the Ising model. It emulates the quantum adiabatic evolution of a network of non-linear Kerr oscillators on classical platforms. These oscillators undergo bifurcation, where each branch corresponds to a spin state, with the network creating an energy imbalance to deter-mine the optimal solution. This approach is highly parallelizable, making it suitable for implementation on GPUs and FPGAs. However, classical emulation introduces analog errors, potentially compromising performance. To address this, alternative methods like ballistic (bSB) and discrete (dSB) evolutions were developed, with the ballistic approach further enhanced by thermal fluctuations (HbSB). Our comprehensive analysis of bSB, dSB, and HbSB using benchmark Max-Cut and knapsack problems aimed to identify the best balance of speed, area, and accuracy. We found that fixed-point number representation allows more algorithm iterations within the same timeframe as floating-point representation. The proposed FPGA architecture implements d$S$B and its heated version, leveraging high parallelizability and efficient memory or-ganization. This design supports larger coefficients and improves problem-solving efficiency without multipliers for matrix-vector evaluation. Challenges include mitigating data dependency between matrix-vector multiplication and time evolution, and developing a preconditioning method for adapting problem coefficients, making the architecture suitable for real-world applications and potential ASIC release.

DOI
10.1109/qce60285.2024.10373

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Simulated Adiabatic Bifurcation (aSB) is a quantum-inspired algorithm that provides approximate solutions for large-scale optimization problems using the Ising model. It emulates the quantum adiabatic evolution of a network of non-linear Kerr oscillators on classical platforms. These oscillators undergo bifurcation, where each branch corresponds to a spin state, with the network creating an energy imbalance to deter-mine the optimal solution. This approach is highly parallelizable, making it suitable for implementation on GPUs and FPGAs. However, classical emulation introduces analog errors, potentially compromising performance. To address this, alternative methods like ballistic (bSB) and discrete (dSB) evolutions were developed, with the ballistic approach further enhanced by thermal fluctuations (HbSB). Our comprehensive analysis of bSB, dSB, and HbSB using benchmark Max-Cut and knapsack problems aimed to identify the best balance of speed, area, and accuracy. We found that fixed-point number representation allows more algorithm iterations within the same timeframe as floating-point representation. The proposed FPGA architecture implements dSSSB and its heated version, leveraging high parallelizability and efficient memory or-ganization. This design supports larger coefficients and improves problem-solving efficiency without multipliers for matrix-vector evaluation. Challenges include mitigating data dependency between matrix-vector multiplication and time evolution, and developing a preconditioning method for adapting problem coefficients, making the architecture suitable for real-world applications and potential ASIC release.
Combinatorial Optimization (CO) problems exhibit exponential complexity, making their resolution challenging. Simulated Adiabatic Bifurcation (aSB) is a quantum-inspired algorithm to obtain approximate solutions to largescale CO problems written in the Ising form. It explores the solution space by emulating the adiabatic evolution of a network of Kerr-nonlinear parametric oscillators (KPOs), where each oscillator represents a variable in the problem. The optimal solution corresponds to the ground state of this system. A key advantage of this approach is the possibility of updating multiple variables simultaneously, making it particularly suited for hardware implementation. To enhance solution quality and convergence speed, variations of the algorithm have been proposed in the literature, including ballistic (bSB), discrete (dSB), and thermal (HbSB) versions. In this work, we have comprehensively analyzed dSB, bSB, and HbSB using dedicated software models, evaluating the feasibility of using a fixed-point representation for hardware implementation. We then present an opensource hardware architecture implementing the dSB algorithm for Field-Programmable Gate Arrays (FPGAs). The design allows users to adjust the degree of algorithmic parallelization based on their specific requirements. A proof-of-concept implementation that solves 256-variable problems was achieved on an AMD Kria KV260 SoM, a low-tier FPGA, validated using well-known max-cut and knapsack problems.
Combinatorial Optimization (CO) problems exhibit exponential complexity, constraining classical computers from providing fast and satisfactory outcomes. Quantum Computers (QCs) can effectively find optimal or near-optimal solutions by exploring the solutions space of a problem encoded in a qubits system, exploiting principles of quantum mechanics. However, non-idealities and high costs limit their availability. These can be overcome by emulating QCs on cheaper and more accessible classical computing platforms, like Field-Programmable Gate Arrays (FPGAs). This article presents a digital architecture, implementing the Ising-compatible Simulated Adiabatic Bifurcation algorithm. It mimics the quantum adiabatic evolution of a network of non-linear Kerr oscillators. The architecture, described in VHDL and targeting FPGAs, consists of processing elements for computing the Kerr oscillators’ evolution, a set of units considering their Ising-related interactions and an evolution variables update unit. The proposed approach includes a speedup-targeting approximation of the algorithm, a method for handling single-variable constraints, and a software model that allows architecture customization for specific problems. Tests were conducted using an Altera Cyclone V SoC with FPGA logic and the Nios II processor for interface purposes. The results demonstrate the functionality of the architecture and its scalability with the problem size, making it suitable for real-world applications.
Combinatorial optimization problems are ubiquitous but difficult to solve. Hardware devices for these problems have recently been developed by various approaches, including quantum computers. Inspired by recently proposed quantum adiabatic optimization using a nonlinear oscillator network, we propose a new optimization algorithm simulating adiabatic evolutions of classical nonlinear Hamiltonian systems exhibiting bifurcation phenomena, which we call simulated bifurcation (SB). SB is based on adiabatic and chaotic (ergodic) evolutions of nonlinear Hamiltonian systems. SB is also suitable for parallel computing because of its simultaneous updating. Implementing SB with a field-programmable gate array, we demonstrate that the SB machine can obtain good approximate solutions of an all-to-all connected 2000-node MAX-CUT problem in 0.5 ms, which is about 10 times faster than a state-of-the-art laser-based machine called a coherent Ising machine. SB will accelerate large-scale combinatorial optimization harnessing digital computer technologies and also offer a new application of computational and mathematical physics.
Since many combinatorial optimization problems can be mapped onto ground-state search problems of Ising models, special-purpose machines for Ising problems have attracted intense attention. Simulated bifurcation (SB) is a recently proposed algorithm to solve these Ising problems. One of the remarkable features of SB is the high-degree parallelism underlying in the algorithm, providing an opportunity to solve the Ising problems very fast by massively parallel processing. In this work, we implement the SB algorithm on FPGAs by designing massively parallel custom circuits. We then compare the FPGA-based SB machines with a state-of-the-art machine called a coherent Ising machine (CIM), a highly optimized implementation of simulated annealing (SA), and GPU-based SB machines. SB machines with spin size of 2,048/4,096 (2K/4K) on an Arria10 GX1150 FPGA have 8,192 processing elements for the matrix-vector multiplication (MM) modules (the most computationally intensive part) and achieve computation throughput of 1,873/2,027 GMAC/s for the MM modules, outperforming 2K/4K SB machines on an Nvidia Tesla V100 GPU (113/183 GMAC/s). The 2K FPGA-SB solves all-to-all connected 2000-node MAX-CUT problem 14X (/124X) faster than the CIM (/the highly-optimized SA), with much better energy efficiency (288X better than the CIM).
Combinatorial optimization problems are economically valuable but computationally hard to solve. Many practical combinatorial optimizations can be converted to the ground-state search problems of Ising spin models. Simulated bifurcation (SB) is a quantum-inspired algorithm to solve these Ising problems. One of the remarkable features of SB is the high-degree parallelism, providing an opportunity for quickly solving those problems by massively parallel processing. In this article, we review our recent works on the design and implementation of high-performance FPGA-based accelerators for SB and their applications toward innovative real-time systems that make optimal responses to ever-changing situations. An example of such an application is an ultrafast financial transaction machine that detects the most profitable cross-currency arbitrage opportunities at microsecond speeds. Also, we show a scale-out architecture for SB-based Ising machines with all-to-all spin-spin couplings that allows continued scaling of both machine size and computational throughput by connecting multiple chips, rather than scaling up a single chip.
Combinatorial optimization problems are economically valuable but computationally hard to solve. Many practical combinatorial optimizations can be converted to the ground-state search problems of Ising spin models. Simulated bifurcation (SB) is a quantum-inspired algorithm to solve these Ising problems. One of the remarkable features of SB is the high-degree parallelism, providing an opportunity for quickly solving those problems by massively parallel processing. In this article, we review our recent works on the design and implementation of high-performance FPGA-based accelerators for SB and their applications toward innovative real-time systems that make optimal responses to ever-changing situations. An example of such an application is an ultrafast financial transaction machine that detects the most profitable cross-currency arbitrage opportunities at microsecond speeds. Also, we show a scale-out architecture for SB-based Ising machines with all-to-all spin-spin couplings that allows continued scaling of both machine size and computational throughput by connecting multiple chips, rather than scaling up a single chip.