Semiconductor Engineering

Classical Computing vs. Machine Learning and Edge AI Techniques in Various Application Domains

Machine Learning (ML) algorithms have revolutionized various domains by enabling data-driven decision-making and automation. The deployment of ML models on embedded edge devices, characterized by ...
Design And Reuse

RISC-V and AI/ML redefining the future of Edge Computing

Easier multi-device coordination: RISC-V facilitates better coordination among multiple edge devices through its open ...
Electronic Design

Bigger Might Not Be Better When It Comes to AI on the Edge

Why it’s important not to over-engineer. Equipped with suitable hardware, IDEs, development tools and kits, frameworks, datasets, and open-source models, engineers can develop ML/AI-enabled, ...
Machine Design

Using AI and Edge Computing for Supply Chain Engineering

With AI and edge computing in the toolbelt, engineers are teasing out actionable insights and applying intelligent automation to increase supply chain agility. AI is a broad category covering software ...
SignalSCV

Emerging Trends in Machine Learning and Their Impact on Modern Computing

Machine learning is no longer just a tech buzzword. Businesses face constant pressure to stay competitive in an ever-changing digital environment. Many feel overwhelmed by the rapid pace of change and ...
Electronic Design

Neuromorphic NPU Sips Power to Handle Edge Machine-Learning Models

What are spiking neural networks (SNNs)? Why the Akida Pico neural processing unit (NPU) can use so little power to handle machine-learning models. Why neuromorphic computing is important to ...