The world is witnessing a revolutionary advancement in artificial intelligence with the emergence of generative AI. Generative AI generates text, images, or other media responding to prompts. We are in the early stages of this new technology; still, the depth and accuracy of its results…
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A Packet-Based Architecture For Edge AI Inference
Despite significant improvements in throughput, edge AI accelerators (Neural Processing Units, or NPUs) are still often underutilized. Inefficient management of weights and activations leads to fewer available cores utilized for multiply-accumulate (MAC) operations. Edge AI applications frequently need to run on small, low-power devices, limiting…
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A Buyers Guide to an NPU
Choosing the right inference NPU (Neural Processing Unit) is a critical decision for a chip architect. There’s a lot at stake because the AI landscape constantly changes, and the choices will impact overall product cost, performance, and long-term viability. There are myriad options regarding system…
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When Little is Better…Introducing the Always-Sensing LittleNPU
At the recent Embedded Vision Summit, Expedera chief scientist and co-founder Sharad Chole detailed LittleNPU, our new AI processing approach for always-sensing smartphones, security cameras, doorbells, and other consumer devices. Always-sensing cameras persistently sample and analyze visual data to identify specific triggers relevant to the…
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Can Compute-In-Memory Bring New Benefits To Artificial Intelligence Inference?
Compute-in-memory (CIM) is not necessarily an Artificial Intelligence (AI) solution; rather, it is a memory management solution. CIM could bring advantages to AI processing by speeding up the multiplication operation at the heart of AI model execution. However, for that to be successful, an AI…
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