The United States National Academy of Engineers has already classified brain emulation as one of the Grand Engineering Challenges. Brain emulation in-silico is a relevant research field for various reasons:


• The immediate benefit of brain emulation is the greater understanding of brain behavior by simulations based on biologically plausible models. Depending on the complexity of the model, it can provide  insight on single-cell behavior to network dynamics of whole brain regions without the need for in-vivo experiments. This can greatly accelerate brain  experimentation and the understanding of the biological mechanisms.

• One important eventual goal of the field is brain rescue. If brain function can be emulated in-silico accurately enough and in real time, it can lead to brain prosthetics and implants  that can recover brain functionality lost due to health conditions and accidents.


The general goal of the project is to apply high performance, innovative solutions enable large scale, accurate or real-time brain simulations and enhance experimental setups or data analysis of the experimental data concerning brain research. Thus, our activities employ a multitude of HPC and other technologies such as FPGAs, Dataflow Computing, GPUs and Many-core processors. The long term goal and main focus of the effort within this theme is the development of a generic tooling framework for accelerated brain simulations, the BrainFrame Framework.

The BrainFrame Project targets the Acceleration of large-scale brain simulations: Focused mainly on cerebellar models, exploring the use of various HPC node technologies such as FPGAs, GPUs, dataflow engines and many-core processors (Xeon Phis) for delivering largely scalable, high-speed simulation platforms.

The BrainFrame Framework

Depending on the desired model characteristics, we identify two general types of simulations that are relevant in neuroscientific experiments. The first one (TYPE-I) has to do with highly accurate (biophysically accurate and even accurate to the molecular level) models of smaller-sized networks (>100 and <1000) that requires real-time or close to real-time performance. The second type involves the simulation of large- or very large-scale networks in which accuracy can often be relaxed. These experiments attempt to simulate network sizes and connection densities closely resembling their biological counterparts (TYPE-II experiments - over 1000 neurons). This, in combination to the variety of models commonly used, makes for a class of applications that vary greatly in terms of workload, while also, depending on the case, requiring high throughput, low latency or both. A single type of HPC fabric, either software- or hardware-based cannot cover all possible use cases with optimal efficiency.

A better approach is to provide scientists with an acceleration platform that has the ability to adjust to the aforementioned variety of workload characteristics. A heterogeneous system that integrates multiple HPC technologies, instead of just one, would be able to provide this. In addition, a framework for a heterogeneous system using a popular user interface for all integrated technologies can also provide the ability to select a different accelerator, depending on availability, cost and performance desired. Such a hardware back-end must overcome additional challenges to be used in the field. It requires a front-end which should provide two crucial features:

An easy and commonly used interface through which neuroscientists can employ the platform, without the constant mediation of an engineer.

A front-end that can reuse the vast amount of models already available to the community.

The eventual goal of the acceleration effort is creating such a heterogeneous back-end, based on Maxeler DFE, Xeon PHI and GPGPU technologies. This backend is combined with a PyNN front-end to implement  the BrainFrame tool-flow.  PyNN, a Python-based, simulator-independent language for specification of brain models, is a widely known and used framework by computational neuroscientists. PyNN is capable of achieving high-speed simulation and it already offers a common interface to popular simulation platforms such as NEURON and NEST as well as newly developing ones that show great future potential, such as NeuroML.




For related publications see: BrainFrame publications 


This work is partially supported by the European Commission Horizon 2020 Framework Programme Project VINEYARD (Gr. Agr. N 687628 ) and ERC-PoC-2014 project BrainFrame (Gr. Agr. N 641000 ). We also like to thank the STFC Hartree Centre (UK) for providing the Maxeler and Xeon-Phi computational resources used in our experiments. We gratefully acknowledge the support of NVidia Corporation with the donation of the GPUs used in this research and the continuous support provided by Maxeler Technologies throughout our research effort.

This project is still ongoing


Contact Persons

  • Bas Koekkoek

  • Christos Strydis

  • Georgios Smaragdos

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