When adding ML capabilities to an edge device where responsiveness or power efficiency is critical, a neural processing unit may be the best solution.
Repurposing a CPU, GPU, or DSP is an easy way to add ML capabilities to an edge device. However, where responsiveness or power efficiency is critical, a dedicated Neural Processing Unit (NPU) may be the best solution. In this paper, we describe how the Arm Ethos-N77 NPU delivers optimal performance.
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AMD CTO Mark Papermaster talks about why heterogeneous architectures will be needed to achieve improvements in PPA.
Steps are being taken to minimize problems, but they will take years to implement.
Companies are speeding ahead to identify the most production-worthy processes for 3D chip stacking.
New capacity planned for 2024, but production will depend on equipment availability.
Number of options is growing, but so is the list of tradeoffs.
Increased transistor density and utilization are creating memory performance issues.
The industry reached an inflection point where analog is getting a fresh look, but digital will not cede ground readily.
Disaggregation and the wind-down of Moore’s Law have changed everything.
FPGAs, CPUs, and equipment receive funding in China; 98 startups raise over $2 billion.
Funding rolls in for photonics and batteries; 88 startups raise $1.3B.
Why UCIe is so important for heterogeneous integration.
Analog foundry expansion; EDA investments; 112 startups raise over $2.6B.
After years of research, chipmakers have started combining ultra low-power designs with advancements in harvesting technology.
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