310 research outputs found
A neuromorphic systems approach to in-memory computing with non-ideal memristive devices: From mitigation to exploitation
Memristive devices represent a promising technology for building neuromorphic
electronic systems. In addition to their compactness and non-volatility
features, they are characterized by computationally relevant physical
properties, such as state-dependence, non-linear conductance changes, and
intrinsic variability in both their switching threshold and conductance values,
that make them ideal devices for emulating the bio-physics of real synapses. In
this paper we present a spiking neural network architecture that supports the
use of memristive devices as synaptic elements, and propose mixed-signal
analog-digital interfacing circuits which mitigate the effect of variability in
their conductance values and exploit their variability in the switching
threshold, for implementing stochastic learning. The effect of device
variability is mitigated by using pairs of memristive devices configured in a
complementary push-pull mechanism and interfaced to a current-mode normalizer
circuit. The stochastic learning mechanism is obtained by mapping the desired
change in synaptic weight into a corresponding switching probability that is
derived from the intrinsic stochastic behavior of memristive devices. We
demonstrate the features of the CMOS circuits and apply the architecture
proposed to a standard neural network hand-written digit classification
benchmark based on the MNIST data-set. We evaluate the performance of the
approach proposed on this benchmark using behavioral-level spiking neural
network simulation, showing both the effect of the reduction in conductance
variability produced by the current-mode normalizer circuit, and the increase
in performance as a function of the number of memristive devices used in each
synapse.Comment: 13 pages, 12 figures, accepted for Faraday Discussion
FPSA: A Full System Stack Solution for Reconfigurable ReRAM-based NN Accelerator Architecture
Neural Network (NN) accelerators with emerging ReRAM (resistive random access
memory) technologies have been investigated as one of the promising solutions
to address the \textit{memory wall} challenge, due to the unique capability of
\textit{processing-in-memory} within ReRAM-crossbar-based processing elements
(PEs). However, the high efficiency and high density advantages of ReRAM have
not been fully utilized due to the huge communication demands among PEs and the
overhead of peripheral circuits.
In this paper, we propose a full system stack solution, composed of a
reconfigurable architecture design, Field Programmable Synapse Array (FPSA) and
its software system including neural synthesizer, temporal-to-spatial mapper,
and placement & routing. We highly leverage the software system to make the
hardware design compact and efficient. To satisfy the high-performance
communication demand, we optimize it with a reconfigurable routing architecture
and the placement & routing tool. To improve the computational density, we
greatly simplify the PE circuit with the spiking schema and then adopt neural
synthesizer to enable the high density computation-resources to support
different kinds of NN operations. In addition, we provide spiking memory blocks
(SMBs) and configurable logic blocks (CLBs) in hardware and leverage the
temporal-to-spatial mapper to utilize them to balance the storage and
computation requirements of NN. Owing to the end-to-end software system, we can
efficiently deploy existing deep neural networks to FPSA. Evaluations show
that, compared to one of state-of-the-art ReRAM-based NN accelerators, PRIME,
the computational density of FPSA improves by 31x; for representative NNs, its
inference performance can achieve up to 1000x speedup.Comment: Accepted by ASPLOS 201
Memristors
This Edited Volume Memristors - Circuits and Applications of Memristor Devices is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of Engineering. The book comprises single chapters authored by various researchers and edited by an expert active in the physical sciences, engineering, and technology research areas. All chapters are complete in itself but united under a common research study topic. This publication aims at providing a thorough overview of the latest research efforts by international authors on physical sciences, engineering, and technology,and open new possible research paths for further novel developments
Neuro-memristive Circuits for Edge Computing: A review
The volume, veracity, variability, and velocity of data produced from the
ever-increasing network of sensors connected to Internet pose challenges for
power management, scalability, and sustainability of cloud computing
infrastructure. Increasing the data processing capability of edge computing
devices at lower power requirements can reduce several overheads for cloud
computing solutions. This paper provides the review of neuromorphic
CMOS-memristive architectures that can be integrated into edge computing
devices. We discuss why the neuromorphic architectures are useful for edge
devices and show the advantages, drawbacks and open problems in the field of
neuro-memristive circuits for edge computing
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
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