💥🕯🤓A boom in low cost edge AI chips using the RISC-V technology is coming says Facebook’s chief AI scientist Yann LeCun
🐜The move to RISC-V for running neural networks for edge AI applications is accelerated by the proposed takeover of ARM by Nvidia, says Yann LeCun, chief AI scientist at Facebook speaking at the Innovation Day of French research lab CEA-Leti.
“There is a change in the industry and ARM with Nvidia makes people uneasy but the emergence of RISC-V sees chips with a RISC-V core and an NPU (neural processing unit),” he said.
🍕“These are incredibly cheap, less than $10, with many out of China, and these will become ubiquitous,” he said. “I’m wondering if RISC-V will take over the world there.”
📟“Certainly edge AI is a super important topic,” he said. “In the next two to three years, it’s not going to be exotic technologies, it’s about reducing the power consumption as much as possible, pruning the neural net, optimising the weights, shutting down parts of the system that aren’t used," said LeCun.
🤿 "The target is AR devices with chips in the next two to three years with devices in the five years, and that’s coming,” he said.
🐜The move to RISC-V for running neural networks for edge AI applications is accelerated by the proposed takeover of ARM by Nvidia, says Yann LeCun, chief AI scientist at Facebook speaking at the Innovation Day of French research lab CEA-Leti.
“There is a change in the industry and ARM with Nvidia makes people uneasy but the emergence of RISC-V sees chips with a RISC-V core and an NPU (neural processing unit),” he said.
🍕“These are incredibly cheap, less than $10, with many out of China, and these will become ubiquitous,” he said. “I’m wondering if RISC-V will take over the world there.”
📟“Certainly edge AI is a super important topic,” he said. “In the next two to three years, it’s not going to be exotic technologies, it’s about reducing the power consumption as much as possible, pruning the neural net, optimising the weights, shutting down parts of the system that aren’t used," said LeCun.
🤿 "The target is AR devices with chips in the next two to three years with devices in the five years, and that’s coming,” he said.
eeNews Europe
RISC-V boom from edge AI says Facebook's chief AI scientist
A boom in low cost edge AI chips using the RISC-V technology is coming says Facebook’s chief AI scientist Yann LeCun
PDP-11🚀
❓ Can I run my Neural Network on the FPGA? ❓ Does Vivado HLS run my CPP code on the FPGA? ❓ What is difference between OneAPI and Intel OpenCL? ❓ Vitis - is it a sort of HLS for VIvado, isn't it? 🤔 There are two main FPGA vendors today - Xilinx and Intel.…
2021_Book_DataParallelC.pdf
15.3 MB
Presentation by Philip Harris & Jeff Krupa (MIT)
Heterogeneous Computing at the LHC
💫 Proton collisions (events) occurs at 40MHz in the CMS detector, a new collision each 25ns and there 8Mb of data per collision and it gives 320Tb/s. There's no chance to catch them all for now.
🐾 There are 3 triggering levels, that select only "interesting event" for offline-computing at rate 8Gb/s. ML Models (Decision Trees and DNNs) are used for events classification. It creates huge challenges both for throughput, and latency requirements.
☁️ Described system integrates FPGAs and GPUs accelerators in the cloud through the network, to make it available for researches.
🧩 This huge and largescale work includes may famous institutions, among them Fermilab, MIT, CERN, AWS and Microsoft Brainwave project and can be applied not only to HEP, but also Astrophysics and Gravitational Wave Detection
- YouTube video
- Slides Link (Dropbox)
Heterogeneous Computing at the LHC
TL;DR🎓 FastML Collaboration is group founded by P.Harris and Nhan Tran to adapt DNN to LHC data flow, but already goes far beyond. HLS4ML tools is part of the project.
💫 Proton collisions (events) occurs at 40MHz in the CMS detector, a new collision each 25ns and there 8Mb of data per collision and it gives 320Tb/s. There's no chance to catch them all for now.
🐾 There are 3 triggering levels, that select only "interesting event" for offline-computing at rate 8Gb/s. ML Models (Decision Trees and DNNs) are used for events classification. It creates huge challenges both for throughput, and latency requirements.
☁️ Described system integrates FPGAs and GPUs accelerators in the cloud through the network, to make it available for researches.
🧩 This huge and largescale work includes may famous institutions, among them Fermilab, MIT, CERN, AWS and Microsoft Brainwave project and can be applied not only to HEP, but also Astrophysics and Gravitational Wave Detection
- YouTube video
- Slides Link (Dropbox)
Apple M1, In-Depth Review
🌽 CPU : 8 ARM cores = 4 high perf + 4 low power , 5nm, TSMC
🥐GPU Comparable with GTX 1650
🍕DRAM : 3DStack HBM, lower latency and power consumption
👉 Read more in Notion
🌽 CPU : 8 ARM cores = 4 high perf + 4 low power , 5nm, TSMC
🥐GPU Comparable with GTX 1650
🍕DRAM : 3DStack HBM, lower latency and power consumption
👉 Read more in Notion