Abstract: Quantization has become a key method for enabling deep learning (DL) inference on resource-constrained embedded systems. As the demand for privacy-preserving, low-latency, and ...

Abstract: Quantization noise is an problem in converting an analog signal to digital and there are two methods called as Rounding and Truncation to minimize the error ...

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 ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

Quantization is a process aimed at simplifying data representation by reducing precision – the number of bits used. This process involves approximating a continuous range of values with a smaller set ...

Imagine looking for similar things based on deeper insights instead of just keywords. That’s what vector databases and similarity searches help with. Vector databases enable vector similarity search.

ABSTRACT: Formulated Atomization Theorems extend the theory of Atomic AString Functions evolving since the 1970s allowing representation of polynomials, complex analytic functions, and solutions of ...