TY - JOUR
T1 - A Real-Time Reconfigurable Multichip Architecture for Large-Scale Biophysically Accurate Neuron Simulation
AU - Zjajjo, Amir
AU - Hofmann, Jaco
AU - Christiaanse, Gerrit Jan
AU - van Eijk, Martijn
AU - Smaragdos, Georgios
AU - Strydis, Christos
AU - Galuzzi, Carlo
AU - van Leuken, Rene
AU - de Graaf, Alexander
PY - 2018
Y1 - 2018
N2 - Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. State-of-the-art neuron simulators are, however, capable of simulating at most few tens/hundreds of biophysically accurate neurons in real-time due to the exponential growth in the interneuron communication costs with the number of simulated neurons. In this paper, we propose a real-time, reconfigurable, multichip system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. All parts of the system are generated automatically, based on the neuron connectivity scheme. Experimental results indicate that the proposed system architecture allows the capacity of over 3000 to 19 200 (depending on the connectivity scheme) biophysically accurate neurons over multiple chips.
AB - Simulation of brain neurons in real-time using biophysically meaningful models is a prerequisite for comprehensive understanding of how neurons process information and communicate with each other, in effect efficiently complementing in-vivo experiments. State-of-the-art neuron simulators are, however, capable of simulating at most few tens/hundreds of biophysically accurate neurons in real-time due to the exponential growth in the interneuron communication costs with the number of simulated neurons. In this paper, we propose a real-time, reconfigurable, multichip system architecture based on localized communication, which effectively reduces the communication cost to a linear growth. All parts of the system are generated automatically, based on the neuron connectivity scheme. Experimental results indicate that the proposed system architecture allows the capacity of over 3000 to 19 200 (depending on the connectivity scheme) biophysically accurate neurons over multiple chips.
KW - Biophysically accurate neuron simulation
KW - multi-chip data-flow architecture
KW - neuron network
UR - http://www.scopus.com/inward/record.url?scp=85041299245&partnerID=8YFLogxK
U2 - 10.1109/TBCAS.2017.2780287
DO - 10.1109/TBCAS.2017.2780287
M3 - Article
C2 - 29570060
AN - SCOPUS:85041299245
SN - 1932-4545
VL - 12
SP - 326
EP - 337
JO - IEEE Transactions on Biomedical Circuits and Systems
JF - IEEE Transactions on Biomedical Circuits and Systems
IS - 2
ER -