Danna 2

Mitchell, J. Parker; Dean, Mark E.; Bruer, Grant; Plank, James S.; Rose, Garrett S.

doi:10.1145/3229884.3229894

Cited by 20 publications

(5 citation statements)

References 5 publications

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“…They can not only maximize the performance of the network, but also minimize the energy and area overheads of the corresponding neuromorphic hardware. They validate the proposed framework using both ANN and SNN models, which involves both deep learning accelerators [e.g., PUMA (Ankit et al, 2019)] and neuromorphic hardware [e.g., DANNA2 (Mitchell et al, 2018) and mrDANNA (Chakma et al, 2017)]. Instead of implementing ANNs and SNNs separately, integration of them has become a promising direction to achieve further breakthroughs toward AGI via complementary advantages (Pei et al, 2019).…”

Section: Efficient Hardwarementioning

confidence: 90%

Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning

Deng

Tang

Roy

2021

Front. Comput. Neurosci.

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Section: Efficient Hardwarementioning

confidence: 90%

Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning

Deng

Tang

Roy

2021

Front. Comput. Neurosci.

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“…For Spiking Neural Networks (SNNs), we consider both digital and mixed-signal hardware; DANNA2 (Mitchell et al, 2018), and mrDANNA (Chakma et al, 2017), respectively. Additionally, we select Pole-balance (Wieland, 1991;Gomez et al, 2006), and RoboNav (Mitchell et al, 2017) for experiments on control applications, and IRIS (Dua and Graff, 2017), and Radio (Reynolds et al, 2018) dataset for classification applications.…”

Section: Methodsmentioning

confidence: 99%

“…We use two different neuromorphic implementations that are already deployed in the TENNLab framework, a fully digital neuromorphic processor, DANNA2 (Mitchell et al, 2018), and a memristive mixed-signal neuromorphic processor, mrDANNA, (Chakma et al, 2017). DANNA2 is a fully digital programmable device with integrate-and-fire neurons and synapses, and mrDANNA is a mixed analog-digital programmable device with metal-oxide memristors.…”

Section: Experimental Setup For Snnmentioning

confidence: 99%

“…To validate H-PABO technique for multi-objective hyperparameter optimization problems in SNN domain, we focus on classification application with IRIS (Dua and Graff, 2017), and Radio (Eggensperger et al, 2013) dataset on both digital (Mitchell et al, 2018), and mixed-signal memristive (Chakma et al, 2017) neuromorphic devices. The summary of the case studies six to nine, and their corresponding HP ranges are given in Tables 4, 2, respectively.…”

Section: Three-objective Optimization With Hierarchical-pabo (H-pabo)mentioning

confidence: 99%

“…For artificial neural networks, we validated our approach using AlexNet (Krizhevsky et al, 2012) and VGG19 (Simonyan and Zisserman, 2014) on a Programmable Ultra-Efficient Memristor-based Accelerator (PUMA, Ankit et al, 2019). For spiking neuromorphic systems, we considered several control and classification tasks such as the canonical pole balancing (Gomez et al, 2006), autonomous robotic navigation (Mitchell et al, 2017), satellite radio signal classification (Reynolds et al, 2018), and Iris dataset classification (Dua and Graff, 2017) on both digital and mixed-signal memristor-based accelerators as the underlying hardware (Chakma et al, 2017;Mitchell et al, 2018;Plank et al, 2018). Hierarchical-PABO predicts the Pareto frontier for a three-objective (network performance, the accelerator's energy efficiency, and area) optimization with relatively few evaluations.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design

et al. 2020

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In resource-constrained environments, such as low-power edge devices and smart sensors, deploying a fast, compact, and accurate intelligent system with minimum energy is indispensable. Embedding intelligence can be achieved using neural networks on neuromorphic hardware. Designing such networks would require determining several inherent hyperparameters. A key challenge is to find the optimum set of hyperparameters that might belong to the input/output encoding modules, the neural network itself, the application, or the underlying hardware. In this work, we present a hierarchical pseudo agent-based multi-objective Bayesian hyperparameter optimization framework (both software and hardware) that not only maximizes the performance of the network, but also minimizes the energy and area requirements of the corresponding neuromorphic hardware. We validate performance of our approach (in terms of accuracy and computation speed) on several control and classification applications on digital and mixed-signal (memristor-based) neural accelerators. We show that the optimum set of hyperparameters might drastically improve the performance of one application (i.e., 52–71% for Pole-Balance), while having minimum effect on another (i.e., 50–53% for RoboNav). In addition, we demonstrate resiliency of different input/output encoding, training neural network, or the underlying accelerator modules in a neuromorphic system to the changes of the hyperparameters.

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A system design perspective on neuromorphic computer processors

Rose¹,

Shawkat²,

Foshie³

et al. 2021

Neuromorph. Comput. Eng.

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Neuromorphic computing has become an attractive candidate for emerging computing platforms. It requires an architectural perspective, meaning the topology or hyperparameters of a neural network is key to realizing sound accuracy and performance in neural networks. However, these network architectures must be executed on some form of computer processor. For machine learning, this is often done with conventional computer processing units, graphics processor units, or some combination thereof. A neuromorphic computer processor or neuroprocessor, in the context of this paper, is a hardware system that has been designed and optimized for executing neural networks of one flavor or another. Here, we review the history of neuromorphic computing and consider various spiking neuroprocessor designs that have emerged over the years. The aim of this paper is to identify emerging trends and techniques in the design of such brain-inspired neuroprocessor computer systems.

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Danna 2

Cited by 20 publications

References 5 publications

Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning

Editorial: Understanding and Bridging the Gap Between Neuromorphic Computing and Machine Learning

Bayesian Multi-objective Hyperparameter Optimization for Accurate, Fast, and Efficient Neural Network Accelerator Design

A system design perspective on neuromorphic computer processors

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