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AI Accelerators

40 summarised stories about AI Accelerators, each linking back to the original source. Browse all topics →

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Friday, 7 August 2026

Practical AI: Don't Work Harder; Work Smarter

EEJournal 3 weeks ago 5

Blaize has developed a Graph Streaming Processor (GSP) designed to reduce unnecessary data movement during AI inference by scheduling computations to keep intermediate data flowing through the processor rather than shuttling it to external memory. The company positions its GSP for the first stage of multi-stage inference pipelines, handling routine tasks efficiently while routing complex cases to GPUs, with the same silicon packaged across embedded modules, PCIe cards, and rack-mount servers. This approach reduces power consumption and hardware costs by matching processor type to workload complexity rather than using expensive GPUs for every computation.

AMD acquires AI chip startup Taalas to boost inference performance by etching models into silicon

The Register 3 weeks ago 28 2 sources

AMD acquired Taalas, a startup that embeds AI model weights directly into silicon to accelerate inference. Taalas's approach delivers performance improvements of 10x or greater compared to standard inference. The acquisition gives AMD proprietary technology to optimize its AI chip offerings against competitors like NVIDIA.

[AINews] AMD buys Taalas

Latent Space 3 weeks ago 39 2 sources

AMD acquired Taalas, a custom AI chip company, according to CEO Lisa Su's decision to pursue vertical integration for inference workloads. The article does not provide a specific acquisition price or date. The move signals AMD's commitment to custom silicon for AI inference, following industry trends toward vertical specialization and custom ASICs rather than general-purpose accelerators.

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