54,921 research outputs found
Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processor
Neuromorphic computing is a new paradigm for design of both the computing
hardware and algorithms inspired by biological neural networks. The event-based
nature and the inherent parallelism make neuromorphic computing a promising
paradigm for building efficient neural network based architectures for control
of fast and agile robots. In this paper, we present a spiking neural network
architecture that uses sensory feedback to control rotational velocity of a
robotic vehicle. When the velocity reaches the target value, the mapping from
the target velocity of the vehicle to the correct motor command, both
represented in the spiking neural network on the neuromorphic device, is
autonomously stored on the device using on-chip plastic synaptic weights. We
validate the controller using a wheel motor of a miniature mobile vehicle and
inertia measurement unit as the sensory feedback and demonstrate online
learning of a simple 'inverse model' in a two-layer spiking neural network on
the neuromorphic chip. The prototype neuromorphic device that features 256
spiking neurons allows us to realise a simple proof of concept architecture for
the purely neuromorphic motor control and learning. The architecture can be
easily scaled-up if a larger neuromorphic device is available.Comment: 6+1 pages, 4 figures, will appear in one of the Robotics conference
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 Bio-Inspired Two-Layer Mixed-Signal Flexible Programmable Chip for Early Vision
A bio-inspired model for an analog programmable array processor (APAP), based on studies on the vertebrate retina, has permitted the realization of complex programmable spatio-temporal dynamics in VLSI. This model mimics the way in which images are processed in the visual pathway, what renders a feasible alternative for the implementation of early vision tasks in standard technologies. A prototype chip has been designed and fabricated in 0.5 μm CMOS. It renders a computing power per silicon area and power consumption that is amongst the highest reported for a single chip. The details of the bio-inspired network model, the analog building block design challenges and trade-offs and some functional tests results are presented in this paper.Office of Naval Research (USA) N-000140210884European Commission IST-1999-19007Ministerio de Ciencia y Tecnología TIC1999-082
Fast synchronization 3R burst-mode receivers for passive optical networks
This paper gives a tutorial overview on high speed burst-mode receiver (BM-RX) requirements, specific for time division multiplexing passive optical networks, and design issues of such BM-RXs as well as their advanced design techniques. It focuses on how to design BM-RXs with short burst overhead for fast synchronization. We present design principles and circuit architectures of various types of burst-mode transimpedance amplifiers, burst-mode limiting amplifiers and burst-mode clock and data recovery circuits. The recent development of 10 Gb/s BM-RXs is highlighted also including dual-rate operation for coexistence with deployed PONs and on-chip auto reset generation to eliminate external timing-critical control signals provided by a PON medium access control. Finally sub-system integration and state-of-the-art system performance for 10 Gb/s PONs are reviewed
A Benes Based NoC Switching Architecture for Mixed Criticality Embedded Systems
Multi-core, Mixed Criticality Embedded (MCE) real-time systems require high
timing precision and predictability to guarantee there will be no interference
between tasks. These guarantees are necessary in application areas such as
avionics and automotive, where task interference or missed deadlines could be
catastrophic, and safety requirements are strict. In modern multi-core systems,
the interconnect becomes a potential point of uncertainty, introducing major
challenges in proving behaviour is always within specified constraints,
limiting the means of growing system performance to add more tasks, or provide
more computational resources to existing tasks.
We present MCENoC, a Network-on-Chip (NoC) switching architecture that
provides innovations to overcome this with predictable, formally verifiable
timing behaviour that is consistent across the whole NoC. We show how the
fundamental properties of Benes networks benefit MCE applications and meet our
architecture requirements. Using SystemVerilog Assertions (SVA), formal
properties are defined that aid the refinement of the specification of the
design as well as enabling the implementation to be exhaustively formally
verified. We demonstrate the performance of the design in terms of size,
throughput and predictability, and discuss the application level considerations
needed to exploit this architecture
Digital Offset Calibration of an OPAMP Towards Improving Static Parameters of 90 nm CMOS DAC
In this paper, an on-chip self-calibrated 8-bit R-2R digital-to-analog converter (DAC) based on digitally compensated input offset of the operational amplifier (OPAMP) is presented. To improve the overall DAC performance, a digital offset cancellation method was used to compensate deviations in the input offset voltage of the OPAMP caused by process variations. The whole DAC as well as offset compensation circuitry were designed in a standard 90 nm CMOS process. The achieved results show that after the self-calibration process, the improvement of 48% in the value of DAC offset error is achieved
Memory and information processing in neuromorphic systems
A striking difference between brain-inspired neuromorphic processors and
current von Neumann processors architectures is the way in which memory and
processing is organized. As Information and Communication Technologies continue
to address the need for increased computational power through the increase of
cores within a digital processor, neuromorphic engineers and scientists can
complement this need by building processor architectures where memory is
distributed with the processing. In this paper we present a survey of
brain-inspired processor architectures that support models of cortical networks
and deep neural networks. These architectures range from serial clocked
implementations of multi-neuron systems to massively parallel asynchronous ones
and from purely digital systems to mixed analog/digital systems which implement
more biological-like models of neurons and synapses together with a suite of
adaptation and learning mechanisms analogous to the ones found in biological
nervous systems. We describe the advantages of the different approaches being
pursued and present the challenges that need to be addressed for building
artificial neural processing systems that can display the richness of behaviors
seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed
neuromorphic computing platforms and system
Dynamic Power Management for Neuromorphic Many-Core Systems
This work presents a dynamic power management architecture for neuromorphic
many core systems such as SpiNNaker. A fast dynamic voltage and frequency
scaling (DVFS) technique is presented which allows the processing elements (PE)
to change their supply voltage and clock frequency individually and
autonomously within less than 100 ns. This is employed by the neuromorphic
simulation software flow, which defines the performance level (PL) of the PE
based on the actual workload within each simulation cycle. A test chip in 28 nm
SLP CMOS technology has been implemented. It includes 4 PEs which can be scaled
from 0.7 V to 1.0 V with frequencies from 125 MHz to 500 MHz at three distinct
PLs. By measurement of three neuromorphic benchmarks it is shown that the total
PE power consumption can be reduced by 75%, with 80% baseline power reduction
and a 50% reduction of energy per neuron and synapse computation, all while
maintaining temporary peak system performance to achieve biological real-time
operation of the system. A numerical model of this power management model is
derived which allows DVFS architecture exploration for neuromorphics. The
proposed technique is to be used for the second generation SpiNNaker
neuromorphic many core system
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