ESL design of customizable real-time neuron networks

Eijk, Martijn F. van; Galuzzi, Carlo; Zjajo, Amir; Smaragdos, Georgios; Strydis, Christos; Leuken, René van

doi:10.1109/biocas.2014.6981815

Cited by 9 publications

(15 citation statements)

References 4 publications

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“…In work done by [9], the extended HH was translated to a Synthesizable-SystemC (SSC) [12] model. In this section the SSC model will be described.…”

Section: The Systemc Modelmentioning

confidence: 99%

“…The original algorithm [9] is shown in Listing 4.5. Using the Vivado HLS scheduler to translate this algorithm immediately into a hardware schedule, results in the data flow shown in Figure 4.11.…”

Section: Dendrite Combine Operationmentioning

confidence: 99%

“…The result given by these computations are used as exponent operands. 9 This buffer is implemented in such a way that the ExpC maximally receives a certain set of operands before they are read from its output buffer.…”

Section: Dendrite Combine Operationmentioning

confidence: 99%

“…In previous implementations [9,17], the exponent architecture is scheduled and implemented by the Vivado HLS tools. This resulted in several large exponent architectures being implemented within the total hardware topology.…”

Section: Exponent Architecturementioning

confidence: 99%

“…Due to the high level of parallelism in neuron networks, reconfigurable hardware, such as a Field Programmable Gate Array (FPGA), can provide the means to properly simulate these intricate and highly parallel networks [9]. However, modern multi-core designs [10,11] have also proved to be capable of simulating large NN's within a given time.…”

mentioning

confidence: 99%

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A real-time hybrid neuron network for highly parallel cognitive systems

Christiaanse

Zjajo

Galuzzi

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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For comprehensive understanding of how neurons communicate with each other, new tools need to be developed that can accurately reproduce and mimic the behaviour of such neurons in real-time. The proposed design in this thesis models an Inferior Olivary Nucleus network on an FPGA device, with a maximised amount of simulated neurons for the given FPGA family type. This has been achieved by the usage of a highly pipelined hybrid neuron network, which executes optimally scheduled floating-point operations that, together with open source IP, has resulted in a cost-effective solutions, capable of simulating responses faster or on par with their biological counterparts.

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“…In work done by [9], the extended HH was translated to a Synthesizable-SystemC (SSC) [12] model. In this section the SSC model will be described.…”

Section: The Systemc Modelmentioning

confidence: 99%

Section: Dendrite Combine Operationmentioning

confidence: 99%

Section: Dendrite Combine Operationmentioning

confidence: 99%

Section: Exponent Architecturementioning

confidence: 99%

mentioning

confidence: 99%

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A real-time hybrid neuron network for highly parallel cognitive systems

Christiaanse

Zjajo

Galuzzi

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Self Cite

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Multi-chip dataflow architecture for massive scale biophysically accurate neuron simulation

Hofmann

Zjajo

Galuzzi

et al. 2016

2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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The ability to simulate brain neurons in real-time using biophysically-meaningful models is a critical pre-requisite grasping human brain behavior. By simulating neurons' behavior, it is possible, for example, to reduce the need for in-vivo experimentation, to improve artificial intelligence and to replace damaged brain parts in patients. A biophysically accurate but complex neuron model, which can be used for such applications, is the Hodgkin-Huxley (HH) model. State of the art simulators are capable of simulating, in real-time, tens of neurons, at most. The currently most advanced simulator is able to simulate 96 HH neurons in real-time. This simulator is limited by its exponential growth in communication costs. To overcome this problem, in this thesis, we propose a new system architecture, which massively increases the amount of neurons which is possible to simulate. By localizing communications, the communication cost is reduced from an exponential to a linear growth with the number of simulated neurons As a result, the proposed system allows the simulation of over 3000 to 19200 cells (depending on the connectivity scheme). To further increase the number of simulated neurons, the proposed system is designed in such a way that it is possible to implement it over multiple chips. Experimental results have shown that it is possible to use up to 8 chips and still keeping the communication costs linear with the number of simulated neurons. The systems is very flexible and allows to tune, during run-time, various parameters, including the presence of connections between neurons, eliminating (or reducing) resynthesis costs, which turn into much faster experimentation cycles. All parts of the system are generated automatically, based on the neuron connectivity scheme. A powerful simulator that incorporates latencies for on and off chip communication, as well as calculation latencies, can be used to find the right configuration for a particular task. As a result, the resulting highly adaptive and configurable system allows for biophysicallyaccurate simulation of massive amounts of cells. The undersigned hereby certify that they have read and recommend to the Faculty of Electrical Engineering, Mathematics and Computer Science for acceptance a thesis entitled "Multi-chip dataflow architecture for massive scale biophysically accurate neuron simulation" by Jaco A. Hofmann in partial fulfillment of the requirements for the degree of Master of Science. AbstractThe ability to simulate brain neurons in real-time using biophysically-meaningful models is a critical pre-requisite grasping human brain behavior. By simulating neurons' behavior, it is possible, for example, to reduce the need for in-vivo experimentation, to improve artificial intelligence and to replace damaged brain parts in patients. A biophysically accurate but complex neuron model, which can be used for such applications, is the Hodgkin-Huxley (HH) model. State of the art simulators are capable of simulating, in real-time, tens of neurons, at most. The...

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Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity

Zjajo

2019

2019 IEEE 11th International Workshop on Computational Intelligence and Applications (IWCIA)

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ESL design of customizable real-time neuron networks

Cited by 9 publications

References 4 publications

A real-time hybrid neuron network for highly parallel cognitive systems

A real-time hybrid neuron network for highly parallel cognitive systems

Multi-chip dataflow architecture for massive scale biophysically accurate neuron simulation

Neurosynaptic Computational Elements for Adaptive Transient Synchrony: Biophysical Accuracy versus Hardware Complexity

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