539 research outputs found
Perspective: Organic electronic materials and devices for neuromorphic engineering
Neuromorphic computing and engineering has been the focus of intense research
efforts that have been intensified recently by the mutation of Information and
Communication Technologies (ICT). In fact, new computing solutions and new
hardware platforms are expected to emerge to answer to the new needs and
challenges of our societies. In this revolution, lots of candidates
technologies are explored and will require leveraging of the pro and cons. In
this perspective paper belonging to the special issue on neuromorphic
engineering of Journal of Applied Physics, we focus on the current achievements
in the field of organic electronics and the potentialities and specificities of
this research field. We highlight how unique material features available
through organic materials can be used to engineer useful and promising
bioinspired devices and circuits. We also discuss about the opportunities that
organic electronic are offering for future research directions in the
neuromorphic engineering field
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
Towards Efficient and Trustworthy AI Through Hardware-Algorithm-Communication Co-Design
Artificial intelligence (AI) algorithms based on neural networks have been
designed for decades with the goal of maximising some measure of accuracy. This
has led to two undesired effects. First, model complexity has risen
exponentially when measured in terms of computation and memory requirements.
Second, state-of-the-art AI models are largely incapable of providing
trustworthy measures of their uncertainty, possibly `hallucinating' their
answers and discouraging their adoption for decision-making in sensitive
applications.
With the goal of realising efficient and trustworthy AI, in this paper we
highlight research directions at the intersection of hardware and software
design that integrate physical insights into computational substrates,
neuroscientific principles concerning efficient information processing,
information-theoretic results on optimal uncertainty quantification, and
communication-theoretic guidelines for distributed processing. Overall, the
paper advocates for novel design methodologies that target not only accuracy
but also uncertainty quantification, while leveraging emerging computing
hardware architectures that move beyond the traditional von Neumann digital
computing paradigm to embrace in-memory, neuromorphic, and quantum computing
technologies. An important overarching principle of the proposed approach is to
view the stochasticity inherent in the computational substrate and in the
communication channels between processors as a resource to be leveraged for the
purpose of representing and processing classical and quantum uncertainty
Accelerate & Actualize: Can 2D Materials Bridge the Gap Between Neuromorphic Hardware and the Human Brain?
Two-dimensional (2D) materials present an exciting opportunity for devices
and systems beyond the von Neumann computing architecture paradigm due to their
diversity of electronic structure, physical properties, and atomically-thin,
van der Waals structures that enable ease of integration with conventional
electronic materials and silicon-based hardware. All major classes of
non-volatile memory (NVM) devices have been demonstrated using 2D materials,
including their operation as synaptic devices for applications in neuromorphic
computing hardware. Their atomically-thin structure, superior physical
properties, i.e., mechanical strength, electrical and thermal conductivity, as
well as gate-tunable electronic properties provide performance advantages and
novel functionality in NVM devices and systems. However, device performance and
variability as compared to incumbent materials and technology remain major
concerns for real applications. Ultimately, the progress of 2D materials as a
novel class of electronic materials and specifically their application in the
area of neuromorphic electronics will depend on their scalable synthesis in
thin-film form with desired crystal quality, defect density, and phase purity.Comment: Neuromorphic Computing, 2D Materials, Heterostructures, Emerging
Memory Devices, Resistive, Phase-Change, Ferroelectric, Ferromagnetic,
Crossbar Array, Machine Learning, Deep Learning, Spiking Neural Network
Toward High Performance, Programmable Extreme-Edge Intelligence for Neuromorphic Vision Sensors utilizing Magnetic Domain Wall Motion-based MTJ
The desire to empower resource-limited edge devices with computer vision (CV)
must overcome the high energy consumption of collecting and processing vast
sensory data. To address the challenge, this work proposes an energy-efficient
non-von-Neumann in-pixel processing solution for neuromorphic vision sensors
employing emerging (X) magnetic domain wall magnetic tunnel junction (MDWMTJ)
for the first time, in conjunction with CMOS-based neuromorphic pixels. Our
hybrid CMOS+X approach performs in-situ massively parallel asynchronous analog
convolution, exhibiting low power consumption and high accuracy across various
CV applications by leveraging the non-volatility and programmability of the
MDWMTJ. Moreover, our developed device-circuit-algorithm co-design framework
captures device constraints (low tunnel-magnetoresistance, low dynamic range)
and circuit constraints (non-linearity, process variation, area consideration)
based on monte-carlo simulations and device parameters utilizing GF22nm FD-SOI
technology. Our experimental results suggest we can achieve an average of 45.3%
reduction in backend-processor energy, maintaining similar front-end energy
compared to the state-of-the-art and high accuracy of 79.17% and 95.99% on the
DVS-CIFAR10 and IBM DVS128-Gesture datasets, respectively.Comment: 11 pages, 7 figures, 2 tabl
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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