Aligning artificial intelligence with climate change mitigation
There is great interest in how the growth of artificial intelligence and machine learning may
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …
affect global GHG emissions. However, such emissions impacts remain uncertain, owing in …
Chasing carbon: The elusive environmental footprint of computing
Given recent algorithm, software, and hardware innovation, computing has enabled a
plethora of new applications. As computing becomes increasingly ubiquitous, however, so …
plethora of new applications. As computing becomes increasingly ubiquitous, however, so …
[HTML][HTML] High-bandwidth density silicon photonic resonators for energy-efficient optical interconnects
The growth of artificial intelligence applications demands ever larger and more complex
deep learning models, dominating today's—and tomorrow's—data center and high …
deep learning models, dominating today's—and tomorrow's—data center and high …
Pushing the limits of narrow precision inferencing at cloud scale with microsoft floating point
B Darvish Rouhani, D Lo, R Zhao… - Advances in neural …, 2020 - proceedings.neurips.cc
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. Through …
datatypes developed for production cloud-scale inferencing on custom hardware. Through …
RecSSD: near data processing for solid state drive based recommendation inference
Neural personalized recommendation models are used across a wide variety of datacenter
applications including search, social media, and entertainment. State-of-the-art models …
applications including search, social media, and entertainment. State-of-the-art models …
Software-hardware co-design for fast and scalable training of deep learning recommendation models
Deep learning recommendation models (DLRMs) have been used across many business-
critical services at Meta and are the single largest AI application in terms of infrastructure …
critical services at Meta and are the single largest AI application in terms of infrastructure …
Autofl: Enabling heterogeneity-aware energy efficient federated learning
YG Kim, CJ Wu - MICRO-54: 54th Annual IEEE/ACM International …, 2021 - dl.acm.org
Federated learning enables a cluster of decentralized mobile devices at the edge to
collaboratively train a shared machine learning model, while keeping all the raw training …
collaboratively train a shared machine learning model, while keeping all the raw training …
Understanding training efficiency of deep learning recommendation models at scale
The use of GPUs has proliferated for machine learning workflows and is now considered
mainstream for many deep learning models. Meanwhile, when training state-of-the-art …
mainstream for many deep learning models. Meanwhile, when training state-of-the-art …
Dreamshard: Generalizable embedding table placement for recommender systems
We study embedding table placement for distributed recommender systems, which aims to
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …
partition and place the tables on multiple hardware devices (eg, GPUs) to balance the …
Autoshard: Automated embedding table sharding for recommender systems
D Zha, L Feng, B Bhushanam, D Choudhary… - Proceedings of the 28th …, 2022 - dl.acm.org
Embedding learning is an important technique in deep recommendation models to map
categorical features to dense vectors. However, the embedding tables often demand an …
categorical features to dense vectors. However, the embedding tables often demand an …