Efficient Optimization Accelerator Framework for Multistate Ising Problems

Ising Machines are a prominent class of hardware architectures that aim to solve NP-hard combinatorial optimization problems. These machines consist of a network of interacting binary spins/neurons that evolve to represent the optimum ground state energy solution. Generally, combinatorial problems are transformed into quadratic unconstrained binary optimization (QUBO) form to harness the computational efficiency of these Ising machines. However, this transformation, especially for multi-state problems, often leads to a more complex exploration landscape than the original problem, thus severely impacting the solution quality. To address this challenge, we model the spin interactions as a generalized boolean logic function to significantly reduce the exploration space. We benchmark the graph coloring problem from the class of multi-state NP-hard optimization using probabilistic Ising solvers to illustrate the effectiveness of our framework. The proposed methodology achieves similar accuracy compared to state-of-the-art heuristics and machine learning algorithms, and demonstrates significant improvement over the existing Ising methods. Additionally, we demonstrate that combining parallel tempering with our existing framework further reduces the coloring error by up to 50% compared to the conventionally used Gibbs sampling algorithm. We also design a 1024-neuron all-to-all connected probabilistic Ising accelerator that shows up to 10000x performance acceleration compared to heuristics while reducing the number of required physical neurons by 1.5-4x compared to conventional Ising machines. Indeed, this accelerator solution demonstrates improvement across all metrics over the current methods, i.e., energy, performance, area, and solution quality. Thus, this work expands the potential of existing Ising hardware to solve a broad class of these multistate optimization problems.

Assessing the Value of 3D Reconstruction in Building Construction

3-dimensional (3D) reconstruction is an emerging field in image processing and computer vision that aims to create 3D visualizations/ models of objects/ scenes from image sets. However, its commercial applications and benefits are yet to be fully explored. In this paper, we describe ongoing work towards assessing the value of 3D reconstruction in the building construction domain. We present preliminary results from a user study, where our objective is to understand the use of visual information in building construction in order to determine problems with the use of visual information and identify potential benefits and scenarios for the use of 3D reconstruction.