Tag: semiconductors
All the articles with the tag "semiconductors".
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The AI Buildout Is Constrained by Its Slowest Layer
Updated:AI capacity becomes real only when chips, racks, cooling, networks, electrical equipment, grid power, permits, and operators arrive together.
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The AI Chips After NVIDIA
Updated:The serious AI chip challengers are not building one replacement for NVIDIA. They are attacking different bottlenecks in memory, latency, power, networking, and software.
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Groq Makes the Compiler Part of the Processor
Groq moves scheduling and data placement from reactive hardware into the compiler, turning a network of SRAM-heavy LPUs into the useful unit of inference capacity.
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The AI Supply Chain Became Part of the Architecture
Accelerator performance and availability now depend on a serial manufacturing system spanning foundry nodes, HBM, chiplets, interposers, substrates, advanced packaging, assembly, and test.
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d-Matrix Moves the Math Into Memory
d-Matrix treats inference as a memory-placement problem, combining digital in-memory compute, an SRAM performance tier, LPDDR capacity, and standard data-center fabrics.
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Cerebras Moved the Cluster Boundary Onto a Wafer
Cerebras removes many chip boundaries with wafer-scale integration, then rebuilds the system around distributed SRAM, streamed weights, rack-scale I/O, and a compiler.
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The GPU Stopped Being the Product
NVIDIA's data-center stack evolved from a fast accelerator into a rack-scale computer spanning compute, memory, interconnect, networking, power, and software.
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Alternative AI Hardware Is a Systems Problem
Huawei, FuriosaAI, and Rebellions show three system boundaries outside the dominant accelerator stack: a complete platform, an efficient inference card, and a scalable NPU system.