AI Infrastructure
AI infrastructure and compute ecosystem
Articles
The Evolution of Intelligent Systems: From Optimization to Automation to AINEWβ10
From optimization to automation to AI, explore how intelligent systems evolved and why understanding this shift matters for tech leaders in 2026.
When Edge AI Lies: Fault Injection and False State in Live Perception PipelinesNEWβ10
Fault injection reveals false states in edge AI perception pipelines, exposing hidden failures that undermine real-time reliability and safety.
Security in the Era of Quantum ComputingNEWβ10
Quantum computing threatens current encryption standards by 2026. Learn about the risks of harvest-now-decrypt-later attacks and the urgent shift to post-quantu...
EBook β Accelerate Silicon Design for Physical AI (Part 1)NEWβ9
Discover how silicon design is evolving for Physical AI in 2026βreal-time processing, energy efficiency, and safety innovations.
Chip Industry Technical Paper Roundup: Sep. 1β9
Chip industry technical paper roundup: latest research, innovations, and breakthroughs in semiconductor engineering from September 1.
Workload-Driven HBF Substrate for Capacity-Scalable LLM Inferenceβ8
Workload-driven HBF substrate enables capacity-scalable LLM inference with 3.2x bandwidth gains and 40% lower energy.
Trust, But Verify: Ensuring Reliability in AI-Driven Semiconductor Designsβ9
Discover how AI transforms chip design and verification in 2026, and why robust strategies like formal and cloud-based testing ensure reliability.
Cycle-Level Simulator for Distributed GPUs in AI Workloads (Purdue)β9
Cycle-level distributed GPU simulator for AI workloads, modeling compute, memory, interconnects, and power for 2026 data center design.
M3D 6T SRAM with BEOL Pass-Gates at 2nm: A Collaborative Breakthrough by Georgia Tech and Synopsysβ8
Georgia Tech and Synopsys unveil 2nm M3D 6T SRAM using BEOL pass-gates, boosting cache density and energy efficiency.
Hybrid HBM-HBF Architecture in LLM Inferenceβ9
Hybrid HBM-HBF architecture for LLM inference cuts memory bottlenecks. Oxford research combines high-bandwidth memory and flash for scalable AI performance.