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Found 15 papers, 2 papers with code
Network architecture search of X-ray based scientific applications
Our NAS and HPS of (1) BraggNN achieves a 31. 03\% improvement in bragg peak detection accuracy with a 87. 57\% reduction in model size, and (2) PtychoNN achieves a 16. 77\% improvement in model accuracy and a 12. 82\% reduction in model size when compared to the baseline PtychoNN model.
Design-Technology Co-Optimization for NVM-based Neuromorphic Processing Elements
First, on the technology front, we propose an optimization scheme where the NVM resistance state that takes the longest time to sense is set on current paths having the least delay, and vice versa, reducing the average PE latency, which improves the QoS.
Implementing Spiking Neural Networks on Neuromorphic Architectures: A Review
Here, we provide a comprehensive overview of such frameworks proposed for both, platform-based design and hardware-software co-design.
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
We propose a system software framework called SentryOS to map SDCNN inference applications to the proposed design.
DFSynthesizer: Dataflow-based Synthesis of Spiking Neural Networks to Neuromorphic Hardware
We propose DFSynthesizer, an end-to-end framework for synthesizing SNN-based machine learning programs to neuromorphic hardware.
Dynamic Reliability Management in Neuromorphic Computing
We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems, by designing an intelligent run-time manager (NCRTM), which dynamically destresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target.
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN).
On the Role of System Software in Energy Management of Neuromorphic Computing
Based on such formulation, we first evaluate the role of a system software in managing the energy consumption of neuromorphic systems.
Compiling Spiking Neural Networks to Mitigate Neuromorphic Hardware Constraints
Spiking Neural Networks (SNNs) are efficient computation models to perform spatio-temporal pattern recognition on {resource}- and {power}-constrained platforms.
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging.
Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
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
First, we propose a greedy technique to partition an SNN into clusters of neurons and synapses such that each cluster can fit on to the resources of a crossbar.
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations.
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Our framework first extracts the precise times at which a charge pump in the hardware is activated to support neural computations within a workload.
Mapping Spiking Neural Networks to Neuromorphic Hardware
SpiNePlacer then finds the best placement of local and global synapses on the hardware using a meta-heuristic-based approach to minimize energy consumption and spike latency.
