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1,390 claims from 864 papers are on the record. 46 have been checked so far; the other 1,344 have no check with a result yet.

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Status: Unchecked ×Keyword: energy efficiency ×Clear all

3 claims from 2 papers

1. Computer Science › Advanced Neural Network Applications

   Drawing Early-Bird Tickets: Towards More Efficient Training of Deep Networks

   You, Li, Xu et al. · arXiv (Cornell University) · 2019

   The paper reports that winning-ticket subnetworks can be identified early in training, and uses this to build more energy-efficient training methods, with savings of up to 4.7x in experiments.

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   1. UncheckedThe paper reports that small 'winning ticket' subnetworks can be found very early in training, using cheap methods such as early stopping and low-precision training.“In this paper, we discover for the first time that the winning tickets can be identified at the very early training stage, which we term as early-bird (EB) tickets, via low-cost training schemes (e.g., early stopping and low-precision training) at large learning rates.”

2. Computer Science › Parallel Computing and Optimization Techniques

   Splitwise: Efficient generative LLM inference using phase splitting

   Patel, Choukse, Zhang et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Based on our extensive characterization, we find that there are two main phases during an LLM inference request: a compute-intensive prompt computation, and a memory-intensive token generation, each with distinct latency, throughput, memory, and power charac…
   2. Unchecked“Specifically, unlike compute-intensive prompt computation phases, token generation phases do not require the compute capability of the latest GPUs, and can be run with lower power and cost.”