Brain fame:From FPGA to heterogeneous acceleration of brain simulations

Among the various methods in neuroscience for understanding brain function, in-silico simulations have been gaining popularity. Advances in neuroscience and engineering led to the creation of mathematical models of networks that do not simply mimic biological behaviour in an abstract fashion but emulate its in significant detail, even to the level of its biophysical properties. Such an example is the Spiking Neural Network (SNN) that can model a variety of additional behavioural features, like encoding data and adapting according to a spike train`s amplitude, frequency and general precise pattern of arrival of spiking events on a neuron. As a result, SNNs have higher explanatory power than their predecessors, thus brain simulations based on SNNs become an attractive topic to explore. In-silico simulations of SNNs can have beneficial results not only for neuroscience research but breakthroughs can also potentially benefit medical, computing and A.I. research. SNNs, though, computationally depending workloads that traditional computing might not be able to cover. Thus, the use of High Performance Computing (HPC) platforms in this application domain becomes desirable. This dissertation explores the topic of HPC-based in-silico brain simulations. Initially, the effort focuses on custom hardware accelerators, due to their potential in providing real-time performance alongside support for large-scale non-real-time experiments and specifically Field Programmable Gate Arrays (FPGAs). The nature of FPGA-based accelerators provides specific benefits against other similar paradigms like Application Specific Integrated Circuit (ASIC) designs.Firstly, we explore the general characteristics of typical SNNs model types to identify their computational requirements in relation to their explanatory strength. We also identify major design characteristics in model development that can directly affect its performance and behaviour when ported to an HPC platform. Subsequently, a detailed literature review is made on FPGA-based SNN implementations. The HPC porting effort begins with the implementation of an extended-Hodgkin-Huxley model of the Inferior-olivary nucleus featuring advanced connectivity. The model is quite demanding and complex enough to act as a realistic benchmark for HPC implementations, while also being scientifically relevant in its own right. FPGA development shows promising performance results not only when doing custom designs but also using High-level synthesis (HLS) toolflows that significantly reduce development time. FPGAs have proven suitable for small-scale embedded-HPC uses as well. The various efforts, though, reveal a very specific weakness of FPGA development that has less to do with the silicon itself and more with its programming environment. The FPGA tools are very inaccessible to non-experts, thus any acceleration effort would require the engineer (and the FPGA development time) to be in the critical path of the research process. An important question to be answered is how the FPGA platform would compare to other popular software-based HPC solutions such as GPU- and CPU-based platforms. A detailed comparison of the best FPGA implementation with GPU and manycore-CPU ports of the same benchmark is conducted. The comparison and evaluation shows that, when it comes to real-time performance, FPGAs have a clear advantage. But for non-real-time, large scale simulations, there is no single platform that can optimally support the complete range of experiments that could be conducted with the inferior olive model. The comparison makes a clear case for BrainFrame, a platform that supports heterogeneous HPC substrates. This dissertation, thus, concludes with the proposal of the BrainFrame system. The proof-of-concept design supports standard and extended Hodgkin-Huxley models, , such as the original inferior-olive model. The system integrates a GPU-, CPU- and FPGA-based HPC back-end while also using a standard neuroscientific language front-end (PyNN) that can score best-in-class performance, alleviate some of the development hurdles and make it far more user-friendly for the typical model developer. Additionally, the multi-node potential of the platform is being explored. BrainFrame provides both a powerful heterogeneous platform for acceleration and also a front-end familiar to the neuroscientist

An architecture for the acceleration of a hybrid leaky integrate and fire SNN on the convey HC-2ex FPGA-based processor

Summarization: Neuromorphic computing is expanding by leaps and bounds through custom integrated circuits (digital and analog), and large scale platforms developed by industry or government-funded projects (e.g. TrueNorth and BrainScaleS, respectively). Whereas the trend is for massive parallelism and neuromorphic computation in order to solve problems, such as those that may appear in machine learning and deep learning algorithms, there is substantial work on brain-like highly accurate neuromorphic computing in order to model the human brain. In such a form of computing, spiking neural networks (SNN) such as the Hodgkin and Huxley model are mapped to various technologies, including FPGAs. In this work, we present a highly efficient FPGA-based architecture for the detailed hybrid Leaky Integrate and Fire SNN that can simulate generic characteristics of neurons of the cerebral cortex. This architecture supports arbitrary, sparse O(n2) interconnection of neurons without need to re-compile the design, and plasticity rules, yielding on a four-FPGA Convey 2ex hybrid computer a speedup of 923x for a non-trivial data set on 240 neurons vs. the same model in the software simulator BRAIN on a Intel(R) Xeon(R) CPU E5-2620 v2 @ 2.10GHz, i.e. the reference state-of-the-art software. Although the reference, official software is single core, the speedup demonstrates that the application scales well among multiple FPGAs, whereas this would not be the case in general-purpose computers due to the arbitrary interconnect requirements. The FPGA-based approach leads to highly detailed models of parts of the human brain up to a few hundred neurons vs. a dozen or fewer neurons on the reference system.Presented on