Extending the neural engineering framework for nonideal silicon synapses

Voelker, Aaron R.; Benjamin, Ben Varkey; Stewart, Terrence C.; Boahen, Kwabena; Eliasmith, Chris

doi:10.1109/iscas.2017.8050810

Cited by 10 publications

(9 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Overall, NEF's functional approach is stated by a set of principles for constructing the neural models, which involves representation, transformation and dynamics (Sharma et al 2016;Voelker et al 2017). Following these principles, the components of a model are the brain-area-inspired functional elements, and its structural pattern is formed by their connections (information flow) as well as the organization of components into hierarchies, subsystems or central mechanisms.…”

Section: Nef (Spaun)mentioning

confidence: 99%

Methodological aspects for cognitive architectures construction: a study and proposal

et al. 2020

View full text Add to dashboard Cite

In the field of Artificial Intelligence (AI), efforts to achieve human-like behavior have taken very different paths through time. Cognitive Architectures (CAs) differentiate from traditional AI approaches, due to their intention to model cognitive and behavioral processes by understanding the brain’s structure and their functionalities in a natural way. However, the development of distinct CAs has not been easy, mainly because there is no consensus on the theoretical basis, assumptions or even purposes for their creation nor how well they reflect human function. In consequence, there is limited information about the methodological aspects to construct this type of models. To address this issue, some initial statements are established to contextualize about the origins and directions of cognitive architectures and their development, which help to outline perspectives, approaches and objectives of this work, supported by a brief study of methodological strategies and historical aspects taken by some of the most relevant architectures to propose a methodology which covers general perspectives for the construction of CAs. This proposal is intended to be flexible, focused on use-case tasks, but also directed by theoretic paradigms or manifestos. A case study between cognitive functions is then detailed, using visual perception and working memory to exemplify the proposal’s assumptions, postulates and binding tools, from their meta-architectural conceptions to validation. Finally, the discussion addresses the challenges found at this stage of development and future work directions.

show abstract

Section: Nef (Spaun)mentioning

confidence: 99%

Methodological aspects for cognitive architectures construction: a study and proposal

et al. 2020

View full text Add to dashboard Cite

show abstract

“…SpiNNaker and BrainScaleS use a simulator-independent Python wrapper, PyNN (Andrew et al, 2009). Sequential mapping is used in SpiNNaker while neural engineering framework (NEF) is used for Neurogrid (Voelker et al, 2017). Neutrams (Ji et al, 2016) implements an optimized mapping technique based on the graph partition problem: Kernighan-Lin (KL) partitioning strategy for network on chip (NoC).…”

Section: Introductionmentioning

confidence: 99%

HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses

et al. 2020

View full text Add to dashboard Cite

The hardware-software co-optimization of neural network architectures is a field of research that emerged with the advent of commercial neuromorphic chips, such as the IBM TrueNorth and Intel Loihi. Development of simulation and automated mapping software tools in tandem with the design of neuromorphic hardware, whilst taking into consideration the hardware constraints, will play an increasingly significant role in deployment of system-level applications. This paper illustrates the importance and benefits of co-design of convolutional neural networks (CNN) that are to be mapped onto neuromorphic hardware with a crossbar array of synapses. Toward this end, we first study which convolution techniques are more hardware friendly and propose different mapping techniques for different convolutions. We show that, for a seven-layered CNN, our proposed mapping technique can reduce the number of cores used by 4.9-13.8 times for crossbar sizes ranging from 128 × 256 to 1,024 × 1,024, and this can be compared to the toeplitz method of mapping. We next develop an iterative co-design process for the systematic design of more hardware-friendly CNNs whilst considering hardware constraints, such as core sizes. A python wrapper, developed for the mapping process, is also useful for validating hardware design and studies on traffic volume and energy consumption. Finally, a new neural network dubbed HFNet is proposed using the above co-design process; it achieves a classification accuracy of 71.3% on the IMAGENET dataset (comparable to the VGG-16) but uses 11 times less cores for neuromorphic hardware with core size of 1,024 × 1,024. We also modified the HFNet to fit onto different core sizes and report on the corresponding classification accuracies. Various aspects of the paper are patent pending.

show abstract

“…Sequential mapping is used in SpiNNaker. Neural engineering framework (NEF) is developed for Neurogrid [10]. Neutrams [11] addresses an optimized mapping technique based on graph partition problem: Kernighan-Lin (KL) partitioning strategy for network on chips.…”

Section: Introductionmentioning

confidence: 99%

MaD: Mapping and debugging framework for implementing deep neural network onto a neuromorphic chip with crossbar array of synapses

Gopalakrishnan,

Kumar,

Chua

2019

Preprint

View full text Add to dashboard Cite

Neuromorphic systems or dedicated hardware for neuromorphic computing is getting popular with the advancement in research on different device materials for synapses, especially in crossbar architecture and also algorithms specific or compatible to neuromorphic hardware. Hence, an automated mapping of any deep neural network onto the neuromorphic chip with crossbar array of synapses and an efficient debugging framework is very essential. Here, mapping is defined as the deployment of a section of deep neural network layer onto a neuromorphic core and the generation of connection lists among population of neurons to specify the connectivity between various neuromorphic cores on the neuromorphic chip. Debugging is the verification of computations performed on the neuromorphic chip during inferencing. Together the framework becomes Mapping and Debugging (MaD) framework. MaD framework is quite general in usage as it is a Python wrapper which can be integrated with almost every simulator tools for neuromorphic chips. This paper illustrates the MaD framework in detail, considering some optimizations while mapping onto a single neuromorphic core. A classification task on MNIST and CIFAR-10 datasets are considered for test case implementation of MaD framework.

show abstract

Extending the neural engineering framework for nonideal silicon synapses

Cited by 10 publications

References 12 publications

Methodological aspects for cognitive architectures construction: a study and proposal

Methodological aspects for cognitive architectures construction: a study and proposal

HFNet: A CNN Architecture Co-designed for Neuromorphic Hardware With a Crossbar Array of Synapses

MaD: Mapping and debugging framework for implementing deep neural network onto a neuromorphic chip with crossbar array of synapses

Contact Info

Product

Resources

About