Abstract
SpiNNaker (Spiking Neural Network Architecture) is a specialised computing engine, intended for real-time simulation of neural systems. It consists of a mesh of 240x240 nodes, each containing 18 ARM9 processors: over a million
cores, communicating via a bespoke network. Ultimately, the machine will support the simulation of up to a billion neurons in real time, allowing simulation experiments to be taken to hitherto unattainable scales. The architecture achieves this by ignoring three of the axioms of computer design: the communication fabric is non-deterministic; there is no global core synchronisation, and the system state - held in distributed memory - is not coherent. Time models itself: there is no notion of computed simulation time - wallclock time is simulation time. Whilst these design decisions are orthogonal to conventional wisdom, they bring the engine behaviour closer to its intended simulation target - neural systems. We describe how SpiNNaker simulates
large neural ensembles; we provide performance figures and outline some failure mechanisms. SpiNNaker simulation time scales 1:1 with wallclock time at least up to 9 million synaptic connections on a 768 core subsystem (~1400th of the full system) to accurately produce logically predicted results.
cores, communicating via a bespoke network. Ultimately, the machine will support the simulation of up to a billion neurons in real time, allowing simulation experiments to be taken to hitherto unattainable scales. The architecture achieves this by ignoring three of the axioms of computer design: the communication fabric is non-deterministic; there is no global core synchronisation, and the system state - held in distributed memory - is not coherent. Time models itself: there is no notion of computed simulation time - wallclock time is simulation time. Whilst these design decisions are orthogonal to conventional wisdom, they bring the engine behaviour closer to its intended simulation target - neural systems. We describe how SpiNNaker simulates
large neural ensembles; we provide performance figures and outline some failure mechanisms. SpiNNaker simulation time scales 1:1 with wallclock time at least up to 9 million synaptic connections on a 768 core subsystem (~1400th of the full system) to accurately produce logically predicted results.
| Original language | English |
|---|---|
| Pages (from-to) | 450-462 |
| Journal | IEEE Transactions on Multi-Scale Computing Systems |
| Volume | 4 |
| Issue number | 3 |
| Early online date | 22 Nov 2017 |
| DOIs | |
| Publication status | Published - 2017 |
Keywords
- Brain modeling
- Computational modeling
- Computer architecture
- Engines
- Event-based computing
- Hardware
- neural system simulation
- neuromorphic computing
- Neurons
- real-time simulation
- Real-time systems
- specialised simulation platforms