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Why hardware-software co-design is the true 100x improvement for AI

MeNews
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[ME AI] News, in a technical interview with Dylan Patel of SemiAnalysis, he pointed out that the biggest improvements in AI performance and efficiency will come from hardware-software co-design, not just manufacturing faster chips. Patel explained that tightly coupling neural network architectures, compilers, and runtime systems with the capabilities of GPUs, TPUs, and emerging AI accelerators can significantly increase throughput and reduce costs. The discussion highlighted examples of how holistic optimizations of memory layout, interconnect usage, and operator fusion can greatly reduce inference latency and training energy consumption. Patel contrasted this approach with a purely hardware-driven approach, where general-purpose accelerators may underperform if the software stack is not tuned for their strengths. The segment also touched on long-term trends, suggesting that future AI platforms will increasingly become vertically integrated, with model design, frameworks, and chips developed together. He believes that this co-design model is crucial for sustaining AI scaling under the power, thermal, and cost constraints of data centers. (Source: MLion)
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