Abstract: Real-world software–hardware co-design for AI accelerators must meet strict constraints on accuracy and PPA, making design space exploration both costly and inefficient. In this work, we ...

Develop optimal solutions to a scheduling problem by modelling it as a Constraint Satisfaction Problem (CSP), a method used widely in the field of Artificial Intelligence. I've open-sourced Delegator ...

When I interviewed dozens of successful writers for a book, I discovered that many of them experience anxiety or fear related to writing. Some of those feelings may be a result of needing to satisfy ...

Barney is a seasoned Data Executive at UniCredit with expertise in financial services, digital banking, AI and data modernization platforms. Successful leaders are not defined by the resources they ...

Many Bayesian Optimization users report that they don’t like that it can’t deal with uncertain factor ranges, lack of availability of rare materials at some stages of experimentation, constrained ...

Physical reservoir computing leverages the intrinsic history-dependence and nonlinearity of hardware to encode spatiotemporal signals directly at the sensor level, enabling low-latency processing of ...

The training error decreases with increasing neuron count and plateaus beyond 28 neurons per hidden layer. For the two-hidden-layer network, error stabilization is ...

Abstract: This article proposes a novel constrained multiobjective evolutionary Bayesian optimization algorithm based on decomposition (named CMOEBO/D) for expensive constrained multiobjective ...

Artificial intelligence (AI) is playing a huge role in heat rate optimization. In some cases, AI-driven models have analyzed operational data to recommend control settings that reduce heat rates by ...

Effective designs of biodegradable polymers are highly desirable for achieving a sustainable society by decreasing environmental burden and replacing petroleum-based resources with biomass. Low-field ...