340 research outputs found
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
Implementation of a Neuromorphic Development Platform with DANNA
Neuromorphic computing is the use of artificial neural networks to solve complex problems. The specialized computing field has been growing in interest during the past few years. Specialized hardware that function as neural networks can be utilized to solve specific problems unsuited for traditional computing architectures such as pattern classification and image recognition. However, these hardware platforms have neural network structures that are static, being limited to only perform a specific application, and cannot be used for other tasks. In this paper, the feasibility of a development platform utilizing a dynamic artificial neural network for researchers is discussed
Many-core and heterogeneous architectures: programming models and compilation toolchains
1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInopartially_openembargoed_20211002Barchi, Francesc
Scalable High-Speed Communications for Neuromorphic Systems
Field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), and other chip/multi-chip level implementations can be used to implement Dynamic Adaptive Neural Network Arrays (DANNA). In some applications, DANNA interfaces with a traditional computing system to provide neural network configuration information, provide network input, process network outputs, and monitor the state of the network. The present host-to-DANNA network communication setup uses a Cypress USB 3.0 peripheral controller (FX3) to enable host-to-array communication over USB 3.0. This communications setup has to run commands in batches and does not have enough bandwidth to meet the maximum throughput requirements of the DANNA device, resulting in output packet loss. Also, the FX3 is unable to scale to support larger single-chip or multi-chip configurations. To alleviate communication limitations and to expand scalability, a new communications solution is presented which takes advantage of the GTX/GTH high-speed serial transceivers found on Xilinx FPGAs. A Xilinx VC707 evaluation kit is used to prototype the new communications board. The high-speed transceivers are used to communicate to the host computer via PCIe and to communicate to the DANNA arrays with the link layer protocol Aurora. The new communications board is able to outperform the FX3, reducing the latency in the communication and increasing the throughput of data. This new communications setup will be used to further DANNA research by allowing the DANNA arrays to scale to larger sizes and for multiple DANNA arrays to be connected to a single communication board
Middleware and Services for Dynamic Adaptive Neural Network Arrays
Dynamic Adaptive Neural Network Arrays (DANNAs) are neuromorphic systems that exhibit spiking behaviors and can be designed using evolutionary optimization. Array elements are rapidly reconfigurable and can function as either neurons or synapses with programmable interconnections and parameters. Visualization applications can examine DANNA element connections, parameters, and functionality, and evolutionary optimization applications can utilize DANNA to speedup neural network simulations. To facilitate interactions with DANNAs from these applications, we have developed a language-agnostic application programming interface (API) that abstracts away low-level communication details with a DANNA and provides a high-level interface for reprogramming and controlling a DANNA. The library has also been designed in modules in order to adapt to future changes in the design of DANNA, including changes to the DANNA element design, DANNA communication protocol, and connection. In addition to communicating with DANNAs, it is also beneficial for applications to store networks with known functionality. Hence, a Representational State Transfer (REST) API with a MongoDB database back-end has been developed to encourage the collection and exploration of networks
AER-based robotic closed-loop control system
Address-Event-Representation (AER) is an
asynchronous protocol for transferring the information of
spiking neuro-inspired systems. Actually AER systems are able
to see, to ear, to process information, and to learn. Regarding to
the actuation step, the AER has been used for implementing
Central Pattern Generator algorithms, but not for controlling
the actuators in a closed-loop spike-based way. In this paper we
analyze an AER based model for a real-time neuro-inspired
closed-loop control system. We demonstrate it into a differential
control system for a two-wheel vehicle using feedback AER
information. PFM modulation has been used to power the DC
motors of the vehicle and translation into AER of encoder
information is also presented for the close-loop. A codesign
platform (called AER-Robot), based into a Xilinx Spartan 3
FPGA and an 8051 USB microcontroller, with power stages for
four DC motors has been used for the demonstrator.Junta de Andalucía P06-TIC-01417Ministerio de Educación y Ciencia TEC2006-11730-C03-0
A FPGA Spike-Based Robot Controlled with Neuro-inspired VITE
This paper presents a spike-based control system applied to a fixed
robotic platform. Our aim is to take a step forward to a future complete spikes
processing architecture, from vision to direct motor actuation. This paper covers
the processing and actuation layer over an anthropomorphic robot. In this way,
the processing layer uses the neuro-inspired VITE algorithm, for reaching a target,
based on PFM taking advantage of spike system information: its frequency.
Thus, all the blocks of the system are based on spikes. Each layer is implemented
within a FPGA board and spikes communication is codified under the
AER protocol. The results show an accurate behavior of the robotic platform
with 6-bit resolution for a 130º range per joint, and an automatic speed control
of the algorithm. Up to 96 motor controllers could be integrated in the same
FPGA, allowing the positioning and object grasping by more complex anthropomorphic
robots.Ministerio de Ciencia e Innovación TEC2009-10639-C04-02Ministerio de Economía y Competitividad TEC2012-37868-C04-0
Review of open neuromorphic architectures and a first integration in the RISC-V PULP platform
Although initially conceived as a tool to empower neuroscientific research by emulating and simulating the human brain, Spiking Neural Networks (SNNs), also known as third generation neural networks, are gaining popularity for their low-power and sparse data processing capabilities. These attributes are valuable for power-constrained edge and Internet of Things (IoT) applications. Several open-source FPGA and ASIC neuromorphic processors have been developed to explore thisfield, although they often require additional computing elements to manage data and communications. In this work, we review recent open-source neuromorphic architectures and the PULP ecosystem. We then present an integration of the ReckOn digitalneuromorphic processor with the PULPissimo RISC-V single core microcontroller to enable edge IoT applications. Our integrated design is validated through QuestaSim hardware simulation. Through this integration of low-power neuromorphic and RISC-V processors, we focus on the promising potential of SNNs for optimizing edge IoT systems constrained by power budgets and data sparsity
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