19 research outputs found
Error-triggered Three-Factor Learning Dynamics for Crossbar Arrays
Recent breakthroughs suggest that local, approximate gradient descent
learning is compatible with Spiking Neural Networks (SNNs). Although SNNs can
be scalably implemented using neuromorphic VLSI, an architecture that can learn
in-situ as accurately as conventional processors is still missing. Here, we
propose a subthreshold circuit architecture designed through insights obtained
from machine learning and computational neuroscience that could achieve such
accuracy. Using a surrogate gradient learning framework, we derive local,
error-triggered learning dynamics compatible with crossbar arrays and the
temporal dynamics of SNNs. The derivation reveals that circuits used for
inference and training dynamics can be shared, which simplifies the circuit and
suppresses the effects of fabrication mismatch. We present SPICE simulations on
XFAB 180nm process, as well as large-scale simulations of the spiking neural
networks on event-based benchmarks, including a gesture recognition task. Our
results show that the number of updates can be reduced hundred-fold compared to
the standard rule while achieving performances that are on par with the
state-of-the-art
Memory Organization for Energy-Efficient Learning and Inference in Digital Neuromorphic Accelerators
The energy efficiency of neuromorphic hardware is greatly affected by the
energy of storing, accessing, and updating synaptic parameters. Various methods
of memory organisation targeting energy-efficient digital accelerators have
been investigated in the past, however, they do not completely encapsulate the
energy costs at a system level. To address this shortcoming and to account for
various overheads, we synthesize the controller and memory for different
encoding schemes and extract the energy costs from these synthesized blocks.
Additionally, we introduce functional encoding for structured connectivity such
as the connectivity in convolutional layers. Functional encoding offers a 58%
reduction in the energy to implement a backward pass and weight update in such
layers compared to existing index-based solutions. We show that for a 2 layer
spiking neural network trained to retain a spatio-temporal pattern, bitmap
(PB-BMP) based organization can encode the sparser networks more efficiently.
This form of encoding delivers a 1.37x improvement in energy efficiency coming
at the cost of a 4% degradation in network retention accuracy as measured by
the van Rossum distance.Comment: submitted to ISCAS202
PCM-Trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials
Dedicated hardware implementations of spiking neural networks that combine the advantages of mixed-signal neuromorphic circuits with those of emerging memory technologies have the potential of enabling ultra-low power pervasive sensory processing. To endow these systems with additional flexibility and the ability to learn to solve specific tasks, it is important to develop appropriate on-chip learning mechanisms.Recently, a new class of three-factor spike-based learning rules have been proposed that can solve the temporal credit assignment problem and approximate the error back-propagation algorithm on complex tasks. However, the efficient implementation of these rules on hybrid CMOS/memristive architectures is still an open challenge. Here we present a new neuromorphic building block,called PCM-trace, which exploits the drift behavior of phase-change materials to implement long lasting eligibility traces, a critical ingredient of three-factor learning rules. We demonstrate how the proposed approach improves the area efficiency by >10X compared to existing solutions and demonstrates a techno-logically plausible learning algorithm supported by experimental data from device measurement
PCM-trace: Scalable Synaptic Eligibility Traces with Resistivity Drift of Phase-Change Materials
Dedicated hardware implementations of spiking neural networks that combine
the advantages of mixed-signal neuromorphic circuits with those of emerging
memory technologies have the potential of enabling ultra-low power pervasive
sensory processing. To endow these systems with additional flexibility and the
ability to learn to solve specific tasks, it is important to develop
appropriate on-chip learning mechanisms.Recently, a new class of three-factor
spike-based learning rules have been proposed that can solve the temporal
credit assignment problem and approximate the error back-propagation algorithm
on complex tasks. However, the efficient implementation of these rules on
hybrid CMOS/memristive architectures is still an open challenge. Here we
present a new neuromorphic building block,called PCM-trace, which exploits the
drift behavior of phase-change materials to implement long lasting eligibility
traces, a critical ingredient of three-factor learning rules. We demonstrate
how the proposed approach improves the area efficiency by >10X compared to
existing solutions and demonstrates a techno-logically plausible learning
algorithm supported by experimental data from device measurementsComment: Typos are fixe
Online Few-shot Gesture Learning on a Neuromorphic Processor
We present the Surrogate-gradient Online Error-triggered Learning (SOEL)
system for online few-shot learningon neuromorphic processors. The SOEL
learning system usesa combination of transfer learning and principles of
computa-tional neuroscience and deep learning. We show that partiallytrained
deep Spiking Neural Networks (SNNs) implemented onneuromorphic hardware can
rapidly adapt online to new classesof data within a domain. SOEL updates
trigger when an erroroccurs, enabling faster learning with fewer updates. Using
gesturerecognition as a case study, we show SOEL can be used for onlinefew-shot
learning of new classes of pre-recorded gesture data andrapid online learning
of new gestures from data streamed livefrom a Dynamic Active-pixel Vision
Sensor to an Intel Loihineuromorphic research processor.Comment: 10 pages, submitted to IEEE JETCAS for revie
Chalcogenide optomemristors for multi-factor neuromorphic computation
This is the final version. Available on open access from Nature Research via the DOI in this recordData availability:
The data presented and used in this publication is available from the corresponding authors on reasonable request.Code availability:
The code presented and used in this publication is available from the corresponding authors on reasonable request.Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation.Engineering and Physical Sciences Research Council (EPSRC)John Fell Fun