TrueNorth: a High-Performance, Low-Power Neurosynaptic Processor for Multi-Sensory Perception, Action, and Cognition
نویسندگان
چکیده
IBM’s TrueNorth neurosynaptic processor is a radical departure from decades of traditional von Neumann computing. Containing 5.4 billion transistors and fabricated in a 28nm low-power CMOS process technology, TrueNorth contains 1 million neurons and 256 million synapses. With applications ranging from embedded and embodied intelligence to large-scale perceptual analysis of streaming multi-sensory data, this massively parallel processor consumes only 65mW typically.
منابع مشابه
Implementing Stochastic Hopfield-Network-based Linear Solvers on a Hardware-Constrained Neural Substrate
IBM’s TrueNorth neurosynaptic system provides an appealing platform for deploying numerical algorithms for ultra-low power, real time, and mobile applications. A recurrent Hopfield neural network is used to solve for the Moore-Penrose matrix pseudoinverse to solve a broad class of linear optimizations. The TrueNorth hardware platform is heavily constrained through weight quantization and severe...
متن کاملReal-Time Scalable Cortical Computing at 46 Giga-Synaptic OPS/Watt with ~100× Speedup in Time-to-Solution and ~100, 000× Reduction in Energy-to-Solution
Drawing on neuroscience, we have developed a parallel, event-driven kernel for neurosynaptic computation, that is efficient with respect to computation, memory, and communication. Building on the previously demonstrated highlyoptimized software expression of the kernel, here, we demonstrate TrueNorth, a co-designed silicon expression of the kernel. TrueNorth achieves five orders of magnitude re...
متن کاملSpiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combines the Asynchronous Time-based Image Sensor with IBM’s TrueNorth Neurosynaptic System is presented. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are...
متن کاملDeep Versus Wide Convolutional Neural Networks for Object Recognition on Neuromorphic System
In the last decade, special purpose computing systems, such as Neuromorphic computing, have become very popular in the field of computer vision and machine learning for classification tasks. In 2015, IBM’s released the TrueNorth Neuromorphic system, kick-starting a new era of Neuromorphic computing. Alternatively, Deep Learning approaches such as Deep Convolutional Neural Networks (DCNN) show a...
متن کاملVisual saliency on networks of neurosynaptic cores
of neurosynaptic cores A. Andreopoulos B. Taba A. S. Cassidy R. Alvarez-Icaza M. D. Flickner W. P. Risk A. Amir P. A. Merolla J. V. Arthur D. J. Berg J. A. Kusnitz P. Datta S. K. Esser R. Appuswamy D. R. Barch D. S. Modha Identifying interesting or salient regions in an image plays an important role for multimedia search, object tracking, active vision, segmentation, and classification. Existin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2016