Alexander Tsyplikhin retweeted
What happens when your devices need a shared way to find each other and be controlled by agents in the physical world? 🤖🤝🤖
Device Connect is your solution - allowing your devices and agents to easily connect, communicate, discover and invoke.
Just look at this demonstration from Arm Everywhere China where an AI agent is able to set up the connection between two Raspberry Pi devices - enabling the camera on one to see a color and the LED on the other to match.
Now it's your turn ➡️ learn.arm.com/learning-paths…
Alexander Tsyplikhin retweeted
Today we have live-stream for talking Claws, Ducks, Robots!
Everyone is invited!
youtube.com/watch?v=o4WJrv4j…
Alexander Tsyplikhin retweeted
Our latest edition of *Papers of the Month* is now available 📚
These are summaries of our team's favourite papers from March, including a new low-rank training procedure GaLore, and the supposed "Era of 1-bit LLMs" (really 1.58 bits)
Mini-version in 🧵
graphcore-research.github.io…
Alexander Tsyplikhin retweeted
8-bit floating point is the next tour de force to train and deploy LLMs.
Today we present our new @graphcoreai preprint:
📢 Training and inference of large language models
using 8-bit floating point 📢
arxiv.org/abs/2309.17224
Details on the 🧵[1/7]
Alexander Tsyplikhin retweeted
Want to receive hands-on training with a novel AI accelerator?
Join the virtual training session:
⚙️ Learn about @graphcoreai hardware & software
💡 See how to run models on Bow Pod at @argonne_lcf AI Testbed
🤖 Get insights into #GNNs & #LLMs
Apply ➡️ hubs.la/Q01Wy19G0
Alexander Tsyplikhin retweeted
Today, we are excited to introduce Graphium into the datamol.io ecosystem!
Graphium is a powerful and flexible Python library for training molecular GNNs at scale.🚀♾️
Try it out today: github.com/datamol-io/graphi…
Alexander Tsyplikhin retweeted
Do you need #LLMs like #GPT-4 and #GPT-3 for every language task? No... fine-tuning open source #GPT-J may be a more cost-effective alternative.
hubs.la/Q01J4g3H0
Alexander Tsyplikhin retweeted
Our new paper Unit Scaling enables FP16/FP8 training *out-of-the-📦*
No need for loss scaling / complex automatic scaling methods 🙌 Hoping to change the way we think about low-precision training.
Paper: arxiv.org/abs/2303.11257
Notebook: ipu.dev/AzbWWv
🧵[1/n]
Alexander Tsyplikhin retweeted
Last (for now), here are the AI research paper counts for the 5 major AI semiconductor startup contenders.
Combined, they're used 125x less than NVIDIA's chips.
@graphcoreai leads the pack, followed by @habanalabs, Cambricon, @cerebras, and @SambaNovaAI.
Alexander Tsyplikhin retweeted
Too bad for u @Microsoft :-), @graphcoreai IPU turns out to be much more than an #AI chip. It can also be used for #HPC apps in computational #astronomy and #seismic imaging. And it does actually pretty good: scoring 2Tflops/s & 1Tflops/s, resp. These are Level-2 #BLAS op! #SRAM
Alexander Tsyplikhin retweeted
Are GPUs the best hardware choice for GNNs? In a new post authored with @emaros96 @Daniels_Data we explore the new @graphcoreai IPU architecture for Temporal GNNs showing it produces 10x gains in performance
michael-bronstein.medium.com…
Alexander Tsyplikhin retweeted
Also on the EfficientNet but w/ groups topic, I've been experimenting with graphcore hardware lately, and, as GC has already shown, EfficientNets w/ groups run well on their hardware and quite well in general graphcore.ai/posts/how-we-ma…
Alexander Tsyplikhin retweeted
In this new paper, researchers from @UMassAmherst, @facebookai and @graphcoreai demonstrate how important COVID-19 analysis using Approximate Bayesian Computation can be massively accelerated with IPUs.
Read the blog:
hubs.ly/H0Cx-j_0
Alexander Tsyplikhin retweeted
Introducing our Second Generation of IPU Systems | Watch as @graphcoreai CEO Nigel Toon announces three new breakthrough technologies for machine intelligence – the IPU-Machine, 7nm GC200 IPU Processor and IPU-POD64
hubs.ly/H0sxg0k0
Alexander Tsyplikhin retweeted
Quant researchers are reliant on speed of execution to foresee threats and drive performance. The @graphcoreai IPU can run algotrading, investment, risk management and fraud detection applications up to 26x faster. Find out how here: hubs.ly/H0pXs2t0
Alexander Tsyplikhin retweeted
S2E1 - Fighting Coronavirus #COVID19 – at Mountain View, CA
Alexander Tsyplikhin retweeted
Hey Silicon Valley!
We do disinfection with professional steam cleaning equipment (300°F) as recommend by the CDC.
Please retweet to reach people and businesses that require our service. – at Mountain View, CA
Alexander Tsyplikhin retweeted
Disinfecting an office kitchen in Mountain View. 300°F steam quickly kills 99.99% bacteria and viruses. – at Mountain View, CA
