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Integrating AI into Embedded Systems Building a Smarter Future: Integrating AI into Embedded Systems Artificial intelligence (AI) has made its way from science fiction into everyday life, revolutionizing industries ranging from healthcare to automotive. AI-powered devices are now an integral part of our lives, driving innovations like smart homes, autonomous vehicles, and personal assistants. However, while AI opens doors to new capabilities, integrating these sophisticated algorithms into embedded systems—small, resource-constrained devices Read Article
CHIPS Act First Big Win is the Apple A16 Vertical CHIPS Act First Big Win is the Apple A16 A $52.7 billion bet is about to pay off. TSMC is starting production of Apple’s A16 SoC in Arizona. About the CHIPS Act The CHIPS and Science Act, passed in 2022, was a landmark initiative designed to boost the domestic production of semiconductors in the United States. With the global semiconductor supply chain heavily concentrated in East Asia—primarily Taiwan, South Korea, and China—the U.S. government recognized the strategic importance of Read Article
Renesas Power Challenges Renesas Is Solving the Power Challenges of AI, EVs, and Data Centers The power demands of cutting-edge technologies are skyrocketing. From artificial intelligence (AI) to electric vehicles (EVs), the world’s energy requirements are exploding, and Renesas is at the forefront of developing solutions. This article explores how Renesas is expanding its portfolio and addressing key power challenges in AI, data centers, and EVs. Renesas: A Powerhouse Beyond Automotive Historically, Renesas has been recognized for its Read Article
Ways to Run an LLM Locally 5 Easy Ways Anyone Can Run an LLM Locally Large Language Models (LLMs) have revolutionized various industries by automating tasks such as content creation, code generation, and document analysis. While many users rely on web-based LLMs like ChatGPT, there's growing interest in running these models locally. Doing so offers key advantages, including enhanced privacy, cost savings, and the ability to fine-tune models for specific applications. Running LLMs locally doesn't need to be Read Article