Publications

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Neuromorphic computing is known for its integration of algorithms and hardware elements that are inspired by the brain. Conventionally, this nontraditional method of computing is used for many neural or learning inspired applications. Unfortunately, this has resulted in the field of neuromorphic computing being relatively narrow in scope. In this paper we discuss two research areas actively trying to widen the impact of neuromorphic systems. The first is Fugu, a high-level programming interface designed to bridge the gap between general computer scientists and those who specialize in neuromorphic areas. The second aims to map classical scientific computing problems onto these frameworks through the example of random walks. This elucidates a class of scientific applications that are conducive to neuromorphic algorithms.

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To safely and reliably operate without a human driver, connected and automated vehicles (CAVs) require more advanced computing hardware and software solutions than are implemented today in vehicles that provide driver-assistance features. A workshop was held to discuss advanced microelectronics and computing approaches that can help meet future energy and computational requirements for CAVs. Workshop questions were posed as follows: will highly automated vehicles be viable with conventional computing approaches or will they require a step-change in computing; what are the energy requirements to support on-board sensing and computing; and what advanced computing approaches could reduce the energy requirements while meeting their computational requirements? At present, there is no clear convergence in the computing architecture for highly automated vehicles. However, workshop participants generally agreed that there is a need to improve the computing performance per watt by at least 10x to advance the degree of automation. Participants suggested that DOE and the national laboratories could play a near-term role by developing benchmarks for determining and comparing CAV computing performance, developing public data sets to support algorithm and software development, and contributing precompetitive advancements in energy efficient computing.

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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.

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Neuromorphic hardware architectures represent a growing family of potential post-Moore's Law Era platforms. Largely due to event-driving processing inspired by the human brain, these computer platforms can offer significant energy benefits compared to traditional von Neumann processors. Unfortunately there still remains considerable difficulty in successfully programming, configuring and deploying neuromorphic systems. We present the Fugu framework as an answer to this need. Rather than necessitating a developer attain intricate knowledge of how to program and exploit spiking neural dynamics to utilize the potential benefits of neuromorphic computing, Fugu is designed to provide a higher level abstraction as a hardware-independent mechanism for linking a variety of scalable spiking neural algorithms from a variety of sources. Individual kernels linked together provide sophisticated processing through compositionality. Fugu is intended to be suitable for a wide-range of neuromorphic applications, including machine learning, scientific computing, and more brain-inspired neural algorithms. Ultimately, we hope the community adopts this and other open standardization attempts allowing for free exchange and easy implementations of the ever-growing list of spiking neural algorithms.

Neuromorphic architectures are represented by a broad class of hardware, with artificial neural network (ANN) architectures at one extreme and event-driven spiking architectures at another. Algorithms and applications efficiently processed by one neuromorphic architecture may be unsuitable for another, but it is challenging to compare various neuromorphic architectures among themselves and with traditional computer architectures. In this position paper, we take inspiration from architectural characterizations in scientific computing and motivate the need for neuromorphic architecture comparison techniques, outline relevant performance metrics and analysis tools, and describe cognitive workloads to meaningfully exercise neuromorphic architectures. Additionally, we propose a simulation-based framework for benchmarking a wide range of neuromorphic workloads. While this work is applicable to neuromorphic development in general, we focus on event-driven architectures, as they offer both unique performance characteristics and evaluation challenges.

The insect brain is a great model system for low power electronics: Insects carry out multisensory integration and are able to change the way the process information, learn, and adapt to changes in their environment with a very limited power budget. This context-dependent processing allows them to implement multiple functionalities within the same network, as well as to minimize power consumption by having context-dependent gains in their first layers of input processing. The combination of low power consumption, adaptability and online learning, and robustness makes them particularly appealing for a number of space applications, from rovers and probes to satellites, all having to deal with the progressive degradation of their capabilities in remote environments. In this work, we explore architectures inspired in the insect brain capable of context-dependent processing and learning. Starting from algorithms, we have explored three different implementations: A spiking implementation in a neuromorphic chip, a custom implementation in an FPGA, and finally hybrid analog/digital implementations based on cross-bar arrays. For the latter, we found that the development of novel resistive materials is crucial in order to enhance the energy efficiency of analog devices while maintaining an adequate footprint. Metal-oxide nanocomposite materials, fabricated using ALD with processes compatible with semiconductor processing, are promising candidates to fill in that role.

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Technological advances have enabled exponential growth in both sensor data collection, as well as computational processing. However, as a limiting factor, the transmission bandwidth in between a space-based sensor and a ground station processing center has not seen the same growth. A resolution to this bandwidth limitation is to move the processing to the sensor, but doing so faces size, weight, and power operational constraints. Different physical constraints on processor manufacturing are spurring a resurgence in neuromorphic approaches amenable to the space-based operational environment. Here we describe historical trends in computer architecture and the implications for neuromorphic computing, as well as give an overview of how remote sensing applications may be impacted by this emerging direction for computing.

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The ability of an intelligent agent to process complex signals such as those found in audio or video depends heavily on the nature of the internal representation of the relevant information. This work explores the mechanisms underlying this process by investigating theories inspired by the function of the neocortex. In particular, we focus on the phenomenon of polychronization, which describes the self-organization in a spiking neural network resulting from the interplay between network structure, driven spiking activity, and synaptic plasticity. What emerges are groups of neurons that exhibit reproducible, time-locked patterns of spiking activity. We propose that this representation is well suited to spatio-temporal signal processing, as it naturally resembles patterns found in real-world signals. We explore the computational properties of this approach and demonstrate the ability of a simple polychronizing network to learn different spatio-temporal signals.

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