The number and variety of test interfaces, coupled with increased packaging complexity, are adding a slew of new challenges.

OpenAI and Broadcom have announced a groundbreaking multi-year collaboration to co-develop and deploy 10 gigawatts of custom AI accelerators and Ethernet networking systems, starting in the second hal...

Artificial Intelligence (AI) has experienced unprecedented expansion in recent years. The spread of AI tools has exceeded the boundaries of data centers and high-performance devices to enter embedded systems ranging from IoT sensors to portable medical devices, up to the most complex industrial applications. Running AI models efficiently in embedded environments has some limitations due to the scarce computational and energy resources available.

Samsung’s zHBM roadmap shows AI memory moving from nearby bandwidth booster to tightly integrated compute partner, reshaping how future accelerators may feed large models.

Alphabet's Google is gearing up for a dramatic surge in custom artificial intelligence silicon production, projecting a rollout of 12 million to 15 million

Oracle is also behind the biggest AI training project out there - Stargate

Taichi has demonstrated high efficiency and complex AI task handling, using a novel hybrid optical approach to overcome scaling issues.

South Korean AI chipmaker FuriosaAI has rejected Meta’s $800M acquisition offer, choosing to remain independent to focus on its cutting-edge AI chip development.

Nvidia Corp. Chief Executive Officer Jensen Huang said the company is firing up manufacturing of H200 AI accelerators for customers in China, a sign of progress in the chipmaker’s effort to reenter th...

How to put the pieces together in a complex design with AI is an unsolved problem.

The new IBM z17 mainframe features AI capabilities across hardware, software, and systems operations.

The hyperscale cloud has evolved from racks of commodity x86 servers into one of the most heterogeneous compute environments ever built. As enterprises pursue increasingly demanding workloads—such as AI training, 5G packet processing, and large-scale data analytics—the classical CPU-centric architecture is no longer sufficient. Cloud providers now rely on an ecosystem of accelerators—GPUs, FPGAs, DPUs, and confidential computing hardware—to unlock performance, efficiency, and new levels of security.