Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Nanowire-based programmable architectures
ACM Journal on Emerging Technologies in Computing Systems (JETC)
Robust Object Recognition with Cortex-Like Mechanisms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling self-developing biological neural networks
Neurocomputing
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Anatomy of a cortical simulator
Proceedings of the 2007 ACM/IEEE conference on Supercomputing
Cortical architectures on a GPGPU
Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units
A convolutional neural network tolerant of synaptic faults for low-power analog hardware
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
Neural Acceleration for General-Purpose Approximate Programs
MICRO-45 Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture
Hardware neural network accelerators
Proceedings of the Ninth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis
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Recent advances in the neuroscientific understanding of the brain are bringing about a tantalizing opportunity for building synthetic machines that perform computation in ways that differ radically from traditional Von Neumann machines. These brain-like architectures, which are premised on our understanding of how the human neocortex computes, are highly fault-tolerant, averaging results over large numbers of potentially faulty components, yet manage to solve very difficult problems more reliably than traditional algorithms. A key principle of operation for these architectures is that of automatic abstraction: independent features are extracted from highly disordered inputs and are used to create abstract invariant representations of the external entities. This feature extraction is applied hierarchically, leading to increasing levels of abstraction at higher levels in the hierarchy. This paper describes and evaluates a biologically plausible computational model for this process, and highlights the inherent fault tolerance of the biologically-inspired algorithm. We introduce a stuck-at fault model for such cortical networks, and describe how this model maps to hardware faults that can occur on commodity GPGPU cores used to realize the model in software. We show experimentally that the model software implementation can intrinsically preserve its functionality in the presence of faulty hardware, without requiring any reprogramming or recompilation. This model is a first step towards developing a comprehensive and biologically plausible understanding of the computational algorithms and microarchitecture of computing systems that mimic the human cortex, and to applying them to the robust implementation of tasks on future computing systems built of faulty components.