Brain Computer Interfaces Benefit from Cloud Advancements

By Kate Ericson

March 23, 2011

What do you get when you mix compute clouds and electroencephalograms (EEG) together? Ask Kathleen Ericson a PhD candidate in the Department of Computer Science at Colorado State University, who in a paper coauthored with Professors Shrideep Pallickara and Charles Anderson has explored some of these possibilities [1]. This paper was awarded the Best Student Paper award at the IEEE Conference on Cloud Computing Technology & Science in December 2010.

Brain Computer Interfaces (BCIs) have been gaining traction in recent years. These applications range from allowing people who have lost voluntary motor control to type at a keyboard [2] and also to allow navigating a wheelchair through a crowded room [3]. These applications rely on EEG data gathered from electrodes held close to the scalp. Machine Learning techniques, such as artificial neural networks, can then be used to interpret the user’s intent from these signals. 

EEG analysis is usually performed in physical proximity to the user that, in turn, can lead to limitations in the processing power available for analyzing the EEG signals. For example, the wheelchair application relies on a laptop carried by the user for all EEG analysis. Professor Anderson has been researching EEG classification problems for several years.

In current BCI applications, there is a one-to-one relationship between the user and the machine.  This usually means that there is a single, very well trained neural network that has been fine-tuned to interacting with that individual. Training a neural network to the point where it can provide meaningful classifications can be time consuming. EEG classification has the additional difficulty that the signals may change over time due to user fatigue. Because of user fatigue, a fine-tuned neural network may need to undergo a period of retraining while in use. 

The CSU team considered an alternative to the approach of a single well-trained neural network: the group of experts approach. This approach involves training many smaller neural networks.  Each network is smaller and less well trained than a single neural network would need to be – meaning that the training process is much shorter. While none of these networks can individually learn enough to accurately classify all data, each learns something slightly different, and an accurate classification can be built upon their predictions as a group. But such an approach also means the need for more compute capabilities.

The decision to moving the EEG analysis to the cloud allowed the team to move away from the one-to-one relationship that is common between users and machines in BCI applications. This is also where the Granules [4, 5] cloud runtime (created by Prof. Pallickara, the author’s PhD advisor) comes in. Granules is uniquely suited to processing such EEG streams. Granules provisions a radically different computation model. Unlike traditional computations that have a run-once semantics, computations in Granules have a lifetime associated with it and can execute multiple times and retain state across multiple executions. This feature comes in particularly handy when you are processing EEG streams in real time.

Using Granules, instead of having a single neural network devoted to classifying EEG signals, one could use a whole cluster. The group of experts’ approach is a particularly good fit for the Map-Reduce paradigm that is supported in Granules. Each mapper is responsible for training and maintaining a neural network.  When a mapper has classified data, it sends its classification on to the reducer. The reducer waits for all mappers to weigh in, and then produces an expert opinion based on the predictions of all the mappers. While their current implementation simply returns the most predicted classification, it is possible to train another neural network on the reducer that can learn which mappers have the best predictions and add appropriate weights to incoming predictions.

While other cloud runtimes, such as Hadoop [6], demand run-once semantics, Granules allows computations to be activated as more data is available (such as new EEG streams being generated), and enter a dormant state between rounds of execution.  Granules is then able to store state between successive rounds of execution.  This allowed the CSU team to train neural networks on a set of resources within Granules, and then stream EEG signals to the cloud for classification.  In Hadoop, this would have required one to write the neural network to file between rounds of execution, and load it back into memory before classifying any data.  This would have precluded the possibility of classifying EEG signals in real time.

An additional benefit of using Granules is the ability to concurrently interleave several long-running computations simultaneously on a given machine.  This means that a single cluster of neural networks can support thousands of users simultaneously.  In their experiments, the team at CSU has supported EEG streams generated by 150 users on a cloud of 10 computers.  In these experiments the streams were generated from a pre-recorded dataset, and were sent in bursts every 250ms for every user.  This resulted in their system classifying EEG streams at the rate of 12MB/s, 1GB/83s, and 1TB in 23 hours. 

This cloud returned classification results in under 250ms (before the next set of data is sent) in 99.9% of the test cases.  With 150 users, 0.04% of the messages were over the 250ms threshold: one of every 2,500 messages (once every 10 minutes) the response to a 250ms packet was too slow. Using compression algorithms on the streams should allow handling even more concurrent users within such a cluster. The design does not preclude allowing the existence of multiple such clusters which would allow the system to scale-out even more.

Ultimately, this research has tremendous promise. By moving EEG analysis to the cloud, one can avoid the limitations many mobile BCI applications have.  Instead of having a single machine dedicated to a single user, one could have a cluster of hundreds of machines serving tens of thousands of users.  This approach has multiple benefits: First, by aggregating so much user data, one can have a much larger base to train the neural networks.  Secondly, one can leverage a group of experts approach – multiple smaller neural networks spread across a cloud can work together to produce an expert opinion.  Lastly, this also opens up possibilities for even more complex analysis with the Map-Reduce paradigm.  EEG streams can be analyzed not only over an immediate interval, but longer intervals of data (such as seconds or minutes) can be analyzed for trends. The Colorado State University team is exploring these research issues.

[1] K. Ericson, et al., “Analyzing Electroencephalograms Using Cloud Computing Techniques,” in IEEE  Conference on Cloud Computing Technology and Science, Indianopolis, USA, 2010.

[2] C. W. Anderson and J. A. Bratman, “Translating Thoughts into Actions by Finding Patterns in Brainwaves,” in Fourteenth Yale Workshop on Adaptive and Learning Systems, New Haven, CT, 2008, pp. 1-6.

[3] F. Galan, et al., “A brain-actuated wheelchair: Asynchronous and non-invasive Brain-computer interfaces for continuous control of robots,” Clinical Neurophysiology, vol. 119, pp. 2159-2169, 2008.

[4] S. Pallickara, et al., “Granules: A Lightweight, Streaming Runtime for Cloud Computing With Support for Map-Reduce,” in IEEE International Conference on Cluster Computing, New Orleans, LA., 2009.

[5] S. Pallickara, et al., “An Overview of the Granules Runtime for Cloud Computing,” in IEEE International Conference on e-Science, Indianapolis, 2008.
[6] T. White, Hadoop: The Definitive Guide, 1 ed.: O’Reilly Media, 2009.

 

Subscribe to HPCwire's Weekly Update!

Be the most informed person in the room! Stay ahead of the tech trends with industry updates delivered to you every week!

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion XL — were added to the benchmark suite as MLPerf continues Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing power it brings to artificial intelligence.  Nvidia's DGX Read more…

Call for Participation in Workshop on Potential NSF CISE Quantum Initiative

March 26, 2024

Editor’s Note: Next month there will be a workshop to discuss what a quantum initiative led by NSF’s Computer, Information Science and Engineering (CISE) directorate could entail. The details are posted below in a Ca Read more…

Waseda U. Researchers Reports New Quantum Algorithm for Speeding Optimization

March 25, 2024

Optimization problems cover a wide range of applications and are often cited as good candidates for quantum computing. However, the execution time for constrained combinatorial optimization applications on quantum device Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at the network layer threatens to make bigger and brawnier pro Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HBM3E memory as well as the the ability to train 1 trillion pa Read more…

MLPerf Inference 4.0 Results Showcase GenAI; Nvidia Still Dominates

March 28, 2024

There were no startling surprises in the latest MLPerf Inference benchmark (4.0) results released yesterday. Two new workloads — Llama 2 and Stable Diffusion Read more…

Q&A with Nvidia’s Chief of DGX Systems on the DGX-GB200 Rack-scale System

March 27, 2024

Pictures of Nvidia's new flagship mega-server, the DGX GB200, on the GTC show floor got favorable reactions on social media for the sheer amount of computing po Read more…

NVLink: Faster Interconnects and Switches to Help Relieve Data Bottlenecks

March 25, 2024

Nvidia’s new Blackwell architecture may have stolen the show this week at the GPU Technology Conference in San Jose, California. But an emerging bottleneck at Read more…

Who is David Blackwell?

March 22, 2024

During GTC24, co-founder and president of NVIDIA Jensen Huang unveiled the Blackwell GPU. This GPU itself is heavily optimized for AI work, boasting 192GB of HB Read more…

Nvidia Looks to Accelerate GenAI Adoption with NIM

March 19, 2024

Today at the GPU Technology Conference, Nvidia launched a new offering aimed at helping customers quickly deploy their generative AI applications in a secure, s Read more…

The Generative AI Future Is Now, Nvidia’s Huang Says

March 19, 2024

We are in the early days of a transformative shift in how business gets done thanks to the advent of generative AI, according to Nvidia CEO and cofounder Jensen Read more…

Nvidia’s New Blackwell GPU Can Train AI Models with Trillions of Parameters

March 18, 2024

Nvidia's latest and fastest GPU, codenamed Blackwell, is here and will underpin the company's AI plans this year. The chip offers performance improvements from Read more…

Nvidia Showcases Quantum Cloud, Expanding Quantum Portfolio at GTC24

March 18, 2024

Nvidia’s barrage of quantum news at GTC24 this week includes new products, signature collaborations, and a new Nvidia Quantum Cloud for quantum developers. Wh Read more…

Alibaba Shuts Down its Quantum Computing Effort

November 30, 2023

In case you missed it, China’s e-commerce giant Alibaba has shut down its quantum computing research effort. It’s not entirely clear what drove the change. Read more…

Nvidia H100: Are 550,000 GPUs Enough for This Year?

August 17, 2023

The GPU Squeeze continues to place a premium on Nvidia H100 GPUs. In a recent Financial Times article, Nvidia reports that it expects to ship 550,000 of its lat Read more…

Shutterstock 1285747942

AMD’s Horsepower-packed MI300X GPU Beats Nvidia’s Upcoming H200

December 7, 2023

AMD and Nvidia are locked in an AI performance battle – much like the gaming GPU performance clash the companies have waged for decades. AMD has claimed it Read more…

DoD Takes a Long View of Quantum Computing

December 19, 2023

Given the large sums tied to expensive weapon systems – think $100-million-plus per F-35 fighter – it’s easy to forget the U.S. Department of Defense is a Read more…

Synopsys Eats Ansys: Does HPC Get Indigestion?

February 8, 2024

Recently, it was announced that Synopsys is buying HPC tool developer Ansys. Started in Pittsburgh, Pa., in 1970 as Swanson Analysis Systems, Inc. (SASI) by John Swanson (and eventually renamed), Ansys serves the CAE (Computer Aided Engineering)/multiphysics engineering simulation market. Read more…

Choosing the Right GPU for LLM Inference and Training

December 11, 2023

Accelerating the training and inference processes of deep learning models is crucial for unleashing their true potential and NVIDIA GPUs have emerged as a game- Read more…

Intel’s Server and PC Chip Development Will Blur After 2025

January 15, 2024

Intel's dealing with much more than chip rivals breathing down its neck; it is simultaneously integrating a bevy of new technologies such as chiplets, artificia Read more…

Baidu Exits Quantum, Closely Following Alibaba’s Earlier Move

January 5, 2024

Reuters reported this week that Baidu, China’s giant e-commerce and services provider, is exiting the quantum computing development arena. Reuters reported � Read more…

Leading Solution Providers

Contributors

Comparing NVIDIA A100 and NVIDIA L40S: Which GPU is Ideal for AI and Graphics-Intensive Workloads?

October 30, 2023

With long lead times for the NVIDIA H100 and A100 GPUs, many organizations are looking at the new NVIDIA L40S GPU, which it’s a new GPU optimized for AI and g Read more…

Shutterstock 1179408610

Google Addresses the Mysteries of Its Hypercomputer 

December 28, 2023

When Google launched its Hypercomputer earlier this month (December 2023), the first reaction was, "Say what?" It turns out that the Hypercomputer is Google's t Read more…

AMD MI3000A

How AMD May Get Across the CUDA Moat

October 5, 2023

When discussing GenAI, the term "GPU" almost always enters the conversation and the topic often moves toward performance and access. Interestingly, the word "GPU" is assumed to mean "Nvidia" products. (As an aside, the popular Nvidia hardware used in GenAI are not technically... Read more…

Shutterstock 1606064203

Meta’s Zuckerberg Puts Its AI Future in the Hands of 600,000 GPUs

January 25, 2024

In under two minutes, Meta's CEO, Mark Zuckerberg, laid out the company's AI plans, which included a plan to build an artificial intelligence system with the eq Read more…

Google Introduces ‘Hypercomputer’ to Its AI Infrastructure

December 11, 2023

Google ran out of monikers to describe its new AI system released on December 7. Supercomputer perhaps wasn't an apt description, so it settled on Hypercomputer Read more…

China Is All In on a RISC-V Future

January 8, 2024

The state of RISC-V in China was discussed in a recent report released by the Jamestown Foundation, a Washington, D.C.-based think tank. The report, entitled "E Read more…

Intel Won’t Have a Xeon Max Chip with New Emerald Rapids CPU

December 14, 2023

As expected, Intel officially announced its 5th generation Xeon server chips codenamed Emerald Rapids at an event in New York City, where the focus was really o Read more…

IBM Quantum Summit: Two New QPUs, Upgraded Qiskit, 10-year Roadmap and More

December 4, 2023

IBM kicks off its annual Quantum Summit today and will announce a broad range of advances including its much-anticipated 1121-qubit Condor QPU, a smaller 133-qu Read more…

  • arrow
  • Click Here for More Headlines
  • arrow
HPCwire