Neuromorphic architectures are emerging not only for real-time simulation of brain-scale biological neural networks but also to support innovative brain-inspired computational paradigms. In both domains there is an increasing demand for flexibility in terms of network configuration and runtime redesign of network parameters and simulated neurons models. Due to the intrinsically high parallelism of these architectures and complexity of the interconnect, broadcasting updates to the cores is time consuming. Hence, static solutions where the network is reloaded from an external host instead of being reconfigured are highly inefficient. To address these requirements, we designed an Application Command Protocol (ACP). The proposed protocol provides a mechanism to remotely trigger the execution of high-level op-codes by the cores and manage their application memory, and supports a more flexible computational model and memory management. We worked on SpiNNaker, a multi-core globally-asynchronous locally-synchronous platform running Spiking Neural Networks (SNNs) simulations. We demonstrated ACP in two SNN applications: i) SNN configuration, where simulation data are efficiently generated through ACP in the memory of computing nodes and ii) SNN reconfiguration, where ACP is used to change SNN network parameters at runtime and to easily switch from learning to test phase in a SNN classification application.
Neuromorphic architectures are emerging not only for real-time simulation of brain-scale biological neural networks but also to support innovative brain-inspired computational paradigms. In both domains there is an increasing demand for flexibility in terms of network configuration and runtime redesign of network parameters and simulated neurons models. Due to the intrinsically high parallelism of these architectures and complexity of the interconnect, broadcasting updates to the cores is time consuming. Hence, static solutions where the network is reloaded from an external host instead of being reconfigured are highly inefficient. To address these requirements, we designed an Application Command Protocol (ACP). The proposed protocol provides a mechanism to remotely trigger the execution of high-level op-codes by the cores and manage their application memory, and supports a more flexible computational model and memory management. We worked on SpiNNaker, a multi-core globally-asynchronous locally-synchronous platform running Spiking Neural Networks (SNNs) simulations. We demonstrated ACP in two SNN applications: i) SNN configuration, where simulation data are efficiently generated through ACP in the memory of computing nodes and ii) SNN reconfiguration, where ACP is used to change SNN network parameters at runtime and to easily switch from learning to test phase in a SNN classification application.
Barchi, F., Urgese, G., Siino, A., Di Cataldo, S., Macii, E., Acquaviva, A. (2019). Flexible on-line reconfiguration of multi-core neuromorphic platforms. IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING, 9(2), 915-927 [10.1109/TETC.2019.2908079].
Flexible on-line reconfiguration of multi-core neuromorphic platforms
Barchi, Francesco
Primo
;Acquaviva, Andrea
2019
Abstract
Neuromorphic architectures are emerging not only for real-time simulation of brain-scale biological neural networks but also to support innovative brain-inspired computational paradigms. In both domains there is an increasing demand for flexibility in terms of network configuration and runtime redesign of network parameters and simulated neurons models. Due to the intrinsically high parallelism of these architectures and complexity of the interconnect, broadcasting updates to the cores is time consuming. Hence, static solutions where the network is reloaded from an external host instead of being reconfigured are highly inefficient. To address these requirements, we designed an Application Command Protocol (ACP). The proposed protocol provides a mechanism to remotely trigger the execution of high-level op-codes by the cores and manage their application memory, and supports a more flexible computational model and memory management. We worked on SpiNNaker, a multi-core globally-asynchronous locally-synchronous platform running Spiking Neural Networks (SNNs) simulations. We demonstrated ACP in two SNN applications: i) SNN configuration, where simulation data are efficiently generated through ACP in the memory of computing nodes and ii) SNN reconfiguration, where ACP is used to change SNN network parameters at runtime and to easily switch from learning to test phase in a SNN classification application.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
