47 citations · 73 across the 12 of their papers we have counts for
5 papers · 1 filter
Compiling Spiking Neural Networks to Mitigate Neuromorphic Hardware Constraints
Adarsha Balaji, Anup Das
Spiking Neural Networks (SNNs) are efficient computation models to perform spatio-temporal pattern recognition on {resource}- and {power}-constrained platforms. SNNs executed on ne…
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
Adarsha Balaji, Shihao Song, Anup Das +5
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is because the…
Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
Adarsha Balaji, Thibaut Marty, Anup Das +1
In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at {run-time}.…
Compiling Spiking Neural Networks to Neuromorphic Hardware
Shihao Song, Adarsha Balaji, Anup Das +2
Machine learning applications that are implemented with spike-based computation model, e.g., Spiking Neural Network (SNN), have a great potential to lower the energy consumption wh…
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
Adarsha Balaji, Prathyusha Adiraju, Hirak J. Kashyap +4
We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two k…