Presentation
A High-Performance Stochastic Simulated Bifurcation Ising Machine
SessionQuantum Real Problem Solver
DescriptionIsing model-based computers have recently emerged as high-performance solvers for combinatorial optimization problems (COPs). For Ising model, a simulated bifurcation (SB) algorithm searches for the solution by solving pairs of differential equations. The SB machine benefits from massive parallelism but suffers from high energy. Dynamic stochastic computing implements accumulation-based operations efficiently. This article proposes a high-performance stochastic SB machine (SSBM) for solving COPs with efficient hardware. To this end, we develop a stochastic SB (sSB) algorithm such that the multiply-and-accumulate (MAC) operation is converted to multiplexing and addition while the numerical integration is implemented by using signed stochastic integrators (SSIs). Specifically, the sSB stochastically ternarizes position values used for the MAC operation. A stochastic computing SB cell (SC-SBC) is constructed by using two SSIs for area efficiency. Additionally, a binary-stochastic computing SB cell (BSC-SBC) uses one binary integrator and one SSI to achieve a reduced delay. Based on sSB, an SSBM is then built by using the SC-SBC or BSC-SBC as the basic building block. The designs and syntheses of two SSBMs with 2000 fully connected spins require at least 1.13 times smaller area than the state-of-the-art designs.
Event Type
Research Manuscript
TimeWednesday, June 2611:42am - 12:00pm PDT
Location3004, 3rd Floor
Design
Quantum Computing