17,010 research outputs found
NeuroFlow: A General Purpose Spiking Neural Network Simulation Platform using Customizable Processors
© 2016 Cheung, Schultz and Luk.NeuroFlow is a scalable spiking neural network simulation platform for off-the-shelf high performance computing systems using customizable hardware processors such as Field-Programmable Gate Arrays (FPGAs). Unlike multi-core processors and application-specific integrated circuits, the processor architecture of NeuroFlow can be redesigned and reconfigured to suit a particular simulation to deliver optimized performance, such as the degree of parallelism to employ. The compilation process supports using PyNN, a simulator-independent neural network description language, to configure the processor. NeuroFlow supports a number of commonly used current or conductance based neuronal models such as integrate-and-fire and Izhikevich models, and the spike-timing-dependent plasticity (STDP) rule for learning. A 6-FPGA system can simulate a network of up to ~600,000 neurons and can achieve a real-time performance of 400,000 neurons. Using one FPGA, NeuroFlow delivers a speedup of up to 33.6 times the speed of an 8-core processor, or 2.83 times the speed of GPU-based platforms. With high flexibility and throughput, NeuroFlow provides a viable environment for large-scale neural network simulation
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems
In this paper we present a methodological framework that meets novel
requirements emerging from upcoming types of accelerated and highly
configurable neuromorphic hardware systems. We describe in detail a device with
45 million programmable and dynamic synapses that is currently under
development, and we sketch the conceptual challenges that arise from taking
this platform into operation. More specifically, we aim at the establishment of
this neuromorphic system as a flexible and neuroscientifically valuable
modeling tool that can be used by non-hardware-experts. We consider various
functional aspects to be crucial for this purpose, and we introduce a
consistent workflow with detailed descriptions of all involved modules that
implement the suggested steps: The integration of the hardware interface into
the simulator-independent model description language PyNN; a fully automated
translation between the PyNN domain and appropriate hardware configurations; an
executable specification of the future neuromorphic system that can be
seamlessly integrated into this biology-to-hardware mapping process as a test
bench for all software layers and possible hardware design modifications; an
evaluation scheme that deploys models from a dedicated benchmark library,
compares the results generated by virtual or prototype hardware devices with
reference software simulations and analyzes the differences. The integration of
these components into one hardware-software workflow provides an ecosystem for
ongoing preparative studies that support the hardware design process and
represents the basis for the maturity of the model-to-hardware mapping
software. The functionality and flexibility of the latter is proven with a
variety of experimental results
Materials science and the sensor revolution
For the past decade, we have been investigating strategies to develop
ways to provide chemical sensing platforms capable of long-term
deployment in remote locations1-3. This key objective has been driven by
the emergence of ubiquitous digital communications and the associated
potential for widely deployed wireless sensor networks (WSNs).
Understandably, in these early days of WSNs, deployments have been
based on very reliable sensors, such as thermistors, accelerometers, flow
meters, photodetectors, and digital cameras. Biosensors and chemical
sensors (bio/chemo-sensors) are largely missing from this rapidly
developing field, despite the obvious value offered by an ability to measure
molecular targets at multiple locations in real-time. Interestingly, while
this paper is focused on the issues with respect to wide area sensing of
the environment, the core challenge is essentially the same for long-term
implantable bio/chemo-sensors4, i.e.; how to maintain the integrity of the
analytical method at a remote, inaccessible location
Digital neural circuits : from ions to networks
PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work
Extending the performance of hybrid NoCs beyond the limitations of network heterogeneity
To meet the performance and scalability demands of the fast-paced technological growth towards exascale and Big-Data processing with the performance bottleneck of conventional metal based interconnects (wireline), alternative interconnect fabrics such as inhomogeneous three-dimensional integrated Network-on-Chip (3D NoC) and hybrid wired-wireless Network-on-Chip (WiNoC) have emanated as a cost-effective solution for emerging System-on-Chip (SoC) design. However, these interconnects trade-off optimized performance for cost by restricting the number of area and power hungry 3D routers and wireless nodes. Moreover, the non-uniform distributed traffic in chip multiprocessor (CMP) demands an on-chip communication infrastructure which can avoid congestion under high traffic conditions while possessing minimal pipeline delay at low-load conditions. To this end, in this paper, we propose a low-latency adaptive router with a low-complexity single-cycle bypassing mechanism to alleviate the performance degradation due to the slow 2D routers in such emerging hybrid NoCs. The proposed router transmits a flit using dimension-ordered routing (DoR) in the bypass datapath at low-loads. When the output port required for intra-dimension bypassing is not available, the packet is routed adaptively to avoid congestion. The router also has a simplified virtual channel allocation (VA) scheme that yields a non-speculative low-latency pipeline. By combining the low-complexity bypassing technique with adaptive routing, the proposed router is able balance the traffic in hybrid NoCs to achieve low-latency communication under various traffic loads. Simulation shows that, the proposed router can reduce applicationsâ execution time by an average of 16.9% compared to low-latency routers such as SWIFT. By reducing the latency between 2D routers (or wired nodes) and 3D routers (or wireless nodes) the proposed router can improve performance efficiency in terms of average packet delay by an average of 45% (or 50%) in 3D NoCs (or WiNoCs)
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