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Found 5 papers, 2 papers with code
Microscaling Data Formats for Deep Learning
Narrow bit-width data formats are key to reducing the computational and storage costs of modern deep learning applications.
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation
AutoGen is an open-source framework that allows developers to build LLM applications via multiple agents that can converse with each other to accomplish tasks.
With Shared Microexponents, A Little Shifting Goes a Long Way
This paper introduces Block Data Representations (BDR), a framework for exploring and evaluating a wide spectrum of narrow-precision formats for deep learning.
Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point
In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of datatypes developed for production cloud-scale inferencing on custom hardware.
Mixed-Signal Charge-Domain Acceleration of Deep Neural networks through Interleaved Bit-Partitioned Arithmetic
Low-power potential of mixed-signal design makes it an alluring option to accelerate Deep Neural Networks (DNNs).
Hardware Architecture
