7,935 research outputs found
Neural networks-on-chip for hybrid bio-electronic systems
PhD ThesisBy modelling the brains computation we can further our understanding
of its function and develop novel treatments for neurological disorders. The
brain is incredibly powerful and energy e cient, but its computation does
not t well with the traditional computer architecture developed over the
previous 70 years. Therefore, there is growing research focus in developing
alternative computing technologies to enhance our neural modelling capability,
with the expectation that the technology in itself will also bene t from
increased awareness of neural computational paradigms.
This thesis focuses upon developing a methodology to study the design
of neural computing systems, with an emphasis on studying systems suitable
for biomedical experiments. The methodology allows for the design to be
optimized according to the application. For example, di erent case studies
highlight how to reduce energy consumption, reduce silicon area, or to
increase network throughput.
High performance processing cores are presented for both Hodgkin-Huxley
and Izhikevich neurons incorporating novel design features. Further, a complete
energy/area model for a neural-network-on-chip is derived, which is
used in two exemplar case-studies: a cortical neural circuit to benchmark
typical system performance, illustrating how a 65,000 neuron network could
be processed in real-time within a 100mW power budget; and a scalable highperformance
processing platform for a cerebellar neural prosthesis. From
these case-studies, the contribution of network granularity towards optimal
neural-network-on-chip performance is explored
A geographically distributed bio-hybrid neural network with memristive plasticity
Throughout evolution the brain has mastered the art of processing real-world
inputs through networks of interlinked spiking neurons. Synapses have emerged
as key elements that, owing to their plasticity, are merging neuron-to-neuron
signalling with memory storage and computation. Electronics has made important
steps in emulating neurons through neuromorphic circuits and synapses with
nanoscale memristors, yet novel applications that interlink them in
heterogeneous bio-inspired and bio-hybrid architectures are just beginning to
materialise. The use of memristive technologies in brain-inspired architectures
for computing or for sensing spiking activity of biological neurons8 are only
recent examples, however interlinking brain and electronic neurons through
plasticity-driven synaptic elements has remained so far in the realm of the
imagination. Here, we demonstrate a bio-hybrid neural network (bNN) where
memristors work as "synaptors" between rat neural circuits and VLSI neurons.
The two fundamental synaptors, from artificial-to-biological (ABsyn) and from
biological-to- artificial (BAsyn), are interconnected over the Internet. The
bNN extends across Europe, collapsing spatial boundaries existing in natural
brain networks and laying the foundations of a new geographically distributed
and evolving architecture: the Internet of Neuro-electronics (IoN).Comment: 16 pages, 10 figure
Principles of Neuromorphic Photonics
In an age overrun with information, the ability to process reams of data has
become crucial. The demand for data will continue to grow as smart gadgets
multiply and become increasingly integrated into our daily lives.
Next-generation industries in artificial intelligence services and
high-performance computing are so far supported by microelectronic platforms.
These data-intensive enterprises rely on continual improvements in hardware.
Their prospects are running up against a stark reality: conventional
one-size-fits-all solutions offered by digital electronics can no longer
satisfy this need, as Moore's law (exponential hardware scaling),
interconnection density, and the von Neumann architecture reach their limits.
With its superior speed and reconfigurability, analog photonics can provide
some relief to these problems; however, complex applications of analog
photonics have remained largely unexplored due to the absence of a robust
photonic integration industry. Recently, the landscape for
commercially-manufacturable photonic chips has been changing rapidly and now
promises to achieve economies of scale previously enjoyed solely by
microelectronics.
The scientific community has set out to build bridges between the domains of
photonic device physics and neural networks, giving rise to the field of
\emph{neuromorphic photonics}. This article reviews the recent progress in
integrated neuromorphic photonics. We provide an overview of neuromorphic
computing, discuss the associated technology (microelectronic and photonic)
platforms and compare their metric performance. We discuss photonic neural
network approaches and challenges for integrated neuromorphic photonic
processors while providing an in-depth description of photonic neurons and a
candidate interconnection architecture. We conclude with a future outlook of
neuro-inspired photonic processing.Comment: 28 pages, 19 figure
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
Asynchronous spiking neurons, the natural key to exploit temporal sparsity
Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is optimized for real-time dynamic signal processing. We believe one important feature of the brain (asynchronous state-full processing) is the key to its excellence in this domain. In this work, we show how asynchronous processing with state-full neurons allows exploitation of the existing sparsity in natural signals. This paper explains three different types of sparsity and proposes an inference algorithm which exploits all types of sparsities in the execution of already trained networks. Our experiments in three different applications (Handwritten digit recognition, Autonomous Steering and Hand-Gesture recognition) show that this model of inference reduces the number of required operations for sparse input data by a factor of one to two orders of magnitudes. Additionally, due to fully asynchronous processing this type of inference can be run on fully distributed and scalable neuromorphic hardware platforms
Characterizing Self-Developing Biological Neural Networks: A First Step Towards their Application To Computing Systems
Carbon nanotubes are often seen as the only alternative technology to silicon
transistors. While they are the most likely short-term one, other longer-term
alternatives should be studied as well. While contemplating biological neurons
as an alternative component may seem preposterous at first sight, significant
recent progress in CMOS-neuron interface suggests this direction may not be
unrealistic; moreover, biological neurons are known to self-assemble into very
large networks capable of complex information processing tasks, something that
has yet to be achieved with other emerging technologies. The first step to
designing computing systems on top of biological neurons is to build an
abstract model of self-assembled biological neural networks, much like computer
architects manipulate abstract models of transistors and circuits. In this
article, we propose a first model of the structure of biological neural
networks. We provide empirical evidence that this model matches the biological
neural networks found in living organisms, and exhibits the small-world graph
structure properties commonly found in many large and self-organized systems,
including biological neural networks. More importantly, we extract the simple
local rules and characteristics governing the growth of such networks, enabling
the development of potentially large but realistic biological neural networks,
as would be needed for complex information processing/computing tasks. Based on
this model, future work will be targeted to understanding the evolution and
learning properties of such networks, and how they can be used to build
computing systems
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
A neuromorphic chip that combines CMOS analog spiking neurons and memristive
synapses offers a promising solution to brain-inspired computing, as it can
provide massive neural network parallelism and density. Previous hybrid analog
CMOS-memristor approaches required extensive CMOS circuitry for training, and
thus eliminated most of the density advantages gained by the adoption of
memristor synapses. Further, they used different waveforms for pre and
post-synaptic spikes that added undesirable circuit overhead. Here we describe
a hardware architecture that can feature a large number of memristor synapses
to learn real-world patterns. We present a versatile CMOS neuron that combines
integrate-and-fire behavior, drives passive memristors and implements
competitive learning in a compact circuit module, and enables in-situ
plasticity in the memristor synapses. We demonstrate handwritten-digits
recognition using the proposed architecture using transistor-level circuit
simulations. As the described neuromorphic architecture is homogeneous, it
realizes a fundamental building block for large-scale energy-efficient
brain-inspired silicon chips that could lead to next-generation cognitive
computing.Comment: This is a preprint of an article accepted for publication in IEEE
Journal on Emerging and Selected Topics in Circuits and Systems, vol 5, no.
2, June 201
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