Abstract: The huge memory and computing costs of deep neural networks (DNNs) greatly hinder their deployment on resource-constrained devices with high efficiency. Quantization has emerged as an ...

点击上方“Deephub Imba”,关注公众号,好文章不错过 !在深度学习落地过程中，有一个常见的误区：一旦推理速度不达标，大家的第一反应往往是拿着模型开到，比如：做剪枝、搞蒸馏、甚至牺牲精度换小模型。实际上生产环境中的 Python ...

Abstract: Quantization is a critical technique employed across various research fields for compressing deep neural networks (DNNs) to facilitate deployment within resource-limited environments. This ...

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...

I'm diving deep into the intersection of infrastructure and machine learning. I'm fascinated by exploring scalable architectures, MLOps, and the latest advancements in AI-driven systems ...

Large language models (LLMs) are increasingly being deployed on edge devices—hardware that processes data locally near the data source, such as smartphones, laptops, and robots. Running LLMs on these ...

A significant bottleneck in large language models (LLMs) that hampers their deployment in real-world applications is the slow inference speeds. LLMs, while powerful, require substantial computational ...

Artificial Intelligence (AI) has seen tremendous growth, transforming industries from healthcare to finance. However, as organizations and researchers develop more advanced models, they face ...

In artificial intelligence, one common challenge is ensuring that language models can process information quickly and efficiently. Imagine you’re trying to use a language model to generate text or ...

I am an AI Reseach Engineer. I was formerly a researcher @Oxford VGG before founding the AI Bites YouTube channel. I am an AI Reseach Engineer. I was formerly a researcher @Oxford VGG before founding ...