Borrowing from nature, neural-inspired interception algorithms were implemented onboard a vehicle. To maximize success, work was conducted in parallel within a simulated environment and on physical hardware. The intercept vehicle used only optical imaging to detect and track the target. A successful outcome is the proof-of-concept demonstration of a neural-inspired algorithm autonomously guiding a vehicle to intercept a moving target. This work tried to establish the key parameters for the intercept algorithm (sensors and vehicle) and expand the knowledge and capabilities of implementing neural-inspired algorithms in simulation and on hardware.
n this presentation we will discuss recent results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia Labs developed Whet stone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.
Deep learning networks have become a vital tool for image and data processing tasks for deployed and edge applications. Resource constraints, particularly low power budgets, have motivated methods and devices for efficient on-edge inference. Two promising methods are reduced precision communication networks (e.g. binary activation spiking neural networks) and weight pruning. In this paper, we provide a preliminary exploration for combining these two methods, specifically in-training weight pruning of whetstone networks, to achieve deep networks with both sparse weights and binary activations.
Parsa, Maryam P.; Schuman, Catherine D.; Date, Prasanna D.; Rose, Derek R.; Kay, Bill K.; Mitchell, J.P.; Young, Steven Y.; Dellana, Ryan A.; Severa, William M.; Potok, Thomas P.; Roy, Kaushik R.
Remote sensing (RS) data collection capabilities are rapidly evolving hyper-spectrally (sensing more spectral bands), hyper-temporally (faster sampling rates) and hyper-spatially (increasing number of smaller pixels). Accordingly, sensor technologies have outpaced transmission capa- bilities introducing a need to process more data at the sensor. While many sophisticated data processing capabilities are emerging, power and other hardware requirements for these approaches on conventional electronic systems place them out of context for resource constrained operational environments. To address these limitations, in this research effort we have investigated and char- acterized neural-inspired architectures to determine suitability for implementing RS algorithms In doing so, we have been able to highlight a 100x performance per watt improvement using neu- romorphic computing as well as developed an algorithmic architecture co-design and exploration capability.
With the successes deep neural networks have achieved across a range of applications, researchers have been exploring computational architectures to more efficiently execute their operation. In addition to the prevalent role of graphics processing units (GPUs), many accelerator architectures have emerged. Neuromorphic is one such particular approach which takes inspiration from the brain to guide the computational principles of the architecture including varying levels of biological realism. In this paper we present results on using the SpiNNaker neuromorphic platform (48-chip model) for deep learning neural network inference. We use the Sandia National Laboratories developed Whetstone spiking deep learning library to train deep multi-layer perceptrons and convolutional neural networks suitable for the spiking substrate on the neural hardware architecture. By using the massively parallel nature of SpiNNaker, we are able to achieve, under certain network topologies, substantial network tiling and consequentially impressive inference throughput. Such high-throughput systems may have eventual application in remote sensing applications where large images need to be chipped, scanned, and processed quickly. Additionally, we explore complex topologies that push the limits of the SpiNNaker routing hardware and investigate how that impacts mapping software-implemented networks to on-hardware instantiations.