Darkblu – Page 3

October 20, 2021

Biologically-inspired Neural Networks for Self-Driving Cars

Imitating the nematode’s nervous system to process information efficiently, this new intelligent system is more robust, more interpretable, and faster to train than current deep neural network architectures with millions of parameters.

Biologically-inspired Neural Networks for Self-Driving Cars

Deep Neural Networks And Other Approaches

Researchers are always looking for new ways to build intelligent models. We all know that really deep supervised models work great when we have sufficient data to train them, but one of the hardest things to do is to generalize well and do it efficiently. We can always go deeper, but it has a high computation cost. So as you may already be thinking, there must be another way to make machines intelligent, needing less data or at least fewer layers in our networks.

One of the most complicated tasks that machine learning researchers and engineers are currently working on is self-driving cars. This is a task where every option needs to be covered, and completely stable, to be able to deploy it on our roads. This process of training a self-driving car typically requires many training examples from real humans as well as a really deep neural network able to understand these data and reproduce the human behaviors in any situation ….

more read : https://www.louisbouchard.ai/mit-biologically-inspired-neural-networks-for-self-driving-cars/

October 3, 2021October 14, 2021

Gorlov helical wind turbine from my 3D printer

The Quietrevolution-Gorlov helical turbine (GHT) is a water turbine evolved from the Darrieus turbine design by altering it to have helical blades/foils. The physical principles of the GHT work are the same as for its main prototype, the Darrieus turbine, and for the family of similar vertical axis wind turbines which includes also Turby wind turbine, aerotecture turbine, Quietrevolution wind turbine, etc. GHT, Turby and Quietrevolution solved pulsatory torque issues by using the helical twist of the blades.

The resulting work, all mechanically printed completely on a 3D printer. A DC motor with a permanent magnet serves as a generator. The motor voltage at the output is 1.8V / 1 RPS.

Wiki: https://en.wikipedia.org/wiki/Quietrevolution_wind_turbine

April 6, 2021April 6, 2021

How to make your own deep learning accelerator chip!

Currently, there are more than 100 companies all over the world building ASICs (Application Specific Integrated Circuit) or SOC’s (System on Chip) targeted towards deep learning applications. There is a long list of companies here. In addition to these startup big companies like Google (TPU), Facebook, Amazon (Inferentia), Tesla etc are all developing custom ASIC’s for deep learning training and inference.
These can be categorized into two types:

Training and Inference — These ASIC’s are designed to handle both training the deep neural network and also performing inference. Training a large neural network like Resnet-50 is a much more compute-intensive task involving gradient descent and back-propagation. Compared to training inference is very simple and requires less computation. NVidia GPU’s, which are most popular today for deep learning, can do both training and inference. Some other examples are Graphcore IPU, Google TPU V3, Cerebras, etc. OpenAI has great analysis showing the recent increase in compute required for training large networks.
Inference — These ASICs are designed to run DNN’s (Deep neural networks) which have been trained on GPU or other ASIC and then trained network is modified (quantized, pruned etc) to run on a different ASIC (like Google Coral Edge TPU, NVidia Jetson Nano). Most people say that the market for deep learning inference is much bigger than the training. Even very small microcontrollers (MCU’s) based on ARM Cortex-M0, M3, M4 etc can do inference as shown by the TensorFlow Lite team.

Making any chip (ASIC, SOC etc) is a costly, difficult and lengthy process typically done by teams of 10 to 1000’s of people depending on the size and complexity of the chip. Here I am only providing a brief overview specific to deep learning inference accelerator. If you have already designed chips you may find this too simple. If you are still interested, read on! If you like it share and 👏 …..
Full article: https://towardsdatascience.com/how-to-make-your-own-deep-learning-accelerator-chip-1ff69b78ece4

February 8, 2021

Orange Pi AI Stick Lite packs 5.6 TOPS Gryfalcon GPU

Shenzhen Xunlong Software’s $19.90 “Orange Pi AI Stick Lite” USB stick features a GTI Lightspeeur SPR2801S NPU at up to 5.6 TOPS @ 100MHz. It’s supported with free, Linux-based AI model transformation tools.

Shenzhen Xunlong Software’s Orange Pi project has released an AI accelerator with a USB stick form factor equipped with Gyrfalcon Technology, Inc.’s Lightspeeur SPR2801S CNN accelerator chip. The Orange Pi AI Stick Lite is designed to accelerate AI inferencing using Caffe and PyTorch frameworks, with TensorFlow support coming soon. It’s optimized for use with Allwinner based Orange Pi SBCs, but the SDK appears to be adaptable to any Linux-driven x86 or Arm-based computer with a USB port.

Orange Pi AI Stick Lite

The Orange Pi AI Stick Lite is a relaunch of an almost identical Orange Pi AI Stick 2801 that was announced in Nov. 2018, according to a CNXSoft post. The previous model cost $69 and required purchasing GTI’s PLAI (People Learning Artificial Intelligence) model transformation tools for $149 to do anything more than run a demo. The new device is not only much cheaper at $19.90, but the PLAI training tools are now free. There’s no download button, however — you must contact the company to get the download link.

GTI’s up to 9.3 TOPS per Watt Lightspeeur SPR2801S is a lower-end sibling to the up to 24-TOPS/W Lightspeeur 2803S NPU, which is built into SolidRun’s i.MX 8M Mini SOM. The “best peak” performance of the 2801S is 5.6 TOPS @ 100MHz. It can also run in an “ultra low power” mode of 2.8 TOPS @ 300mW. GTI also offers a mid-range Lightspeeur 2802 model at up to 9.9 TOPS/W.

The 28nm fabricated, 7 x 7mm Lightspeeur SPR2801S has an SDIO 3.0 interface and eMMC 4.5 storage. It offers read bandwidth of 68MB/s and write bandwidth of 84.69 MB/s. The NPU includes a 2-dimensional Matrix Processing Engine (MPE) featuring an APiM (AI Processing in Memory) technology that uses magnetoresistive random access memory (MRAM) …..

sources: http://linuxgizmos.com/orange-pi-ai-stick-lite-taps-5-6-tops-gryfalcon-gpu/