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AI Safety Tip: if You Don’t Want It Giving Bioweapon Instructions, Maybe Don’t Put Them in the Training Data, Say Researchers

100% Center coverage: 1 sources

New research shows that scrubbing risky material from AI training data can build safeguards that are harder to bypass — and one author calls out tech giants for keeping such work under wraps.See the Story

Apple Analyst Rethinks Price Target After $100 Billion U.S. Investment

In This Article: Apple analyst rethinks price target after $100 billion U.S. investment originally appeared on TheStreet. All hands on deck! Captain Cook is taking the helm. Please note that we're not talking about the 18th century explorer James Cook, who led three voyages to the Southern and…Donald Trump · United States See the Story

Energy-Efficient Signal Detectors For Massive MIMO Using SRAM-Based IMCs (Univ. of Illinois at Urbana–Champaign)

A new technical paper titled “Energy-Accuracy Trade-Offs in Massive MIMO Signal Detection Using SRAM-Based In-Memory Computing” was published by researchers at the University of Illinois at Urbana–Champaign. Abstract “This paper investigates the use of SRAM-based in-memory computing (IMC) architectures for designing energy efficient and accurate signal detectors for massive multi-input multi-output (MIMO) systems. SRAM-based IMCs are the state-o…Urbana, Illinois · Urbana Read Article

Microservice-Based LLM Agents Enable EDA Flow Automation (Duke Univ. and Univ. of Maryland)

A new technical paper titled “AutoEDA: Enabling EDA Flow Automation through Microservice-Based LLM Agents” was published by researchers at Duke University and University of Maryland. Abstract “Modern Electronic Design Automation (EDA) workflows, especially the RTL-to-GDSII flow, require heavily manual scripting and demonstrate a multitude of tool-specific interactions which limits scalability and efficiency. While LLMs introduces strides for aut…Durham, North Carolina · Durham Read Article

Server-Scale Programmable Photonic Fabric to Interconnect Accelerators Within Servers (Cornell University, Lightmatter)

A new technical paper titled “Morphlux: Programmable chip-to-chip photonic fabrics in multi-accelerator servers for ML” was published by researchers at Cornell University and Lightmatter. Abstract “We optically interconnect accelerator chips (e.g., GPUs, TPUs) within compute servers using newly viable programmable chip-to-chip photonic fabrics. In contrast, today, commercial multi-accelerator compute servers that are workhorses of ML, use electr…Ithaca, New York · Ithaca Read Article

Power Gating Enabling in NPUs (Univ. of Illinois Urbana-Champaign)

A new technical paper titled “ReGate: Enabling Power Gating in Neural Processing Units” was published by researchers at the University of Illinois Urbana-Champaign. Abstract “The energy efficiency of neural processing units (NPU) is playing a critical role in developing sustainable data centers. Our study with different generations of NPU chips reveals that 30%–72% of their energy consumption is contributed by static power dissipation, due to th…Illinois · Illinois Read Article

The Severity Of Test Escapes And SDCs Caused By Them (Google)

A new technical paper titled “Silent Data Corruption by 10x Test Escapes Threatens Reliable Computing” was published by Google. Abstract “Too many defective compute chips are escaping existing manufacturing tests — at least an order of magnitude more than industrial targets across all compute chip types in data centers. Silent data corruptions (SDCs) caused by test escapes, when left unaddressed, pose a major threat to reliable computing. We pre…Google · Mountain View Read Article

AI, From A To Z

First in a seven-part series: What’s the difference between AI, ML, DL, LLMs, and agentic AI? Is it truly revolutionary, or is it an evolutionary series of steps that have enabled machines to do much more than in the past? Jon Herlocker, founder, vice president and general manager of Tignis (now part of Cohu), talks about the evolution of AI over nearly 70 years, the chain of innovation that has enabled things like ChatGPT and agentic AI, and ho…Artificial Intelligence Read Article

System-Level Design For 1.6 Tbps Interoperability In AI Data Centers

By Madhumita Sanyal and Diwakar Kumaraswamy The rapid escalation of AI/ML workloads—driven by increasingly large language models—is reshaping high-performance computing and AI data center architectures. Real-time inference and large-scale training are pushing the limits of compute and interconnect performance. With model sizes and parameter counts doubling every 4–6 months, infrastructure must provide extreme computational throughput along with …Artificial Intelligence Read Article

News from Semiconductor Engineering

News from Semiconductor Engineering