21 research outputs found
VLSI Architecture for Configurable and Low-Complexity Design of Hard-Decision Viterbi Decoding Algorithm
Convolutional encoding and data decoding are fundamental processes in convolutional error correction. One of the most popular error correction methods in decoding is the Viterbi algorithm. It is extensively implemented in many digital communication applications. Its VLSI design challenges are about area, speed, power, complexity and configurability. In this research, we specifically propose a VLSI architecture for a configurable and low-complexity design of a hard-decision Viterbi decoding algorithm. The configurable and low-complexity design is achieved by designing a generic VLSI architecture, optimizing each processing element (PE) at the logical operation level and designing a conditional adapter. The proposed design can be configured for any predefined number of trace-backs, only by changing the trace-back parameter value. Its computational process only needs NÂ +Â 2 clock cycles latency, with N is the number of trace-backs. Its configurability function has been proven for NÂ =Â 8, NÂ =Â 16, NÂ =Â 32 and NÂ =Â 64. Furthermore, the proposed design was synthesized and evaluated in Xilinx and Altera FPGA target boards for area consumption and speed performance
SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments
Spiking Neural Networks (SNNs) bear the potential of efficient unsupervised
and continual learning capabilities because of their biological plausibility,
but their complexity still poses a serious research challenge to enable their
energy-efficient design for resource-constrained scenarios (like embedded
systems, IoT-Edge, etc.). We propose SpikeDyn, a comprehensive framework for
energy-efficient SNNs with continual and unsupervised learning capabilities in
dynamic environments, for both the training and inference phases. It is
achieved through the following multiple diverse mechanisms: 1) reduction of
neuronal operations, by replacing the inhibitory neurons with direct lateral
inhibitions; 2) a memory- and energy-constrained SNN model search algorithm
that employs analytical models to estimate the memory footprint and energy
consumption of different candidate SNN models and selects a Pareto-optimal SNN
model; and 3) a lightweight continual and unsupervised learning algorithm that
employs adaptive learning rates, adaptive membrane threshold potential, weight
decay, and reduction of spurious updates. Our experimental results show that,
for a network with 400 excitatory neurons, our SpikeDyn reduces the energy
consumption on average by 51% for training and by 37% for inference, as
compared to the state-of-the-art. Due to the improved learning algorithm,
SpikeDyn provides on avg. 21% accuracy improvement over the state-of-the-art,
for classifying the most recently learned task, and by 8% on average for the
previously learned tasks.Comment: To appear at the 58th IEEE/ACM Design Automation Conference (DAC),
December 2021, San Francisco, CA, US
Q-SpiNN: A Framework for Quantizing Spiking Neural Networks
A prominent technique for reducing the memory footprint of Spiking Neural
Networks (SNNs) without decreasing the accuracy significantly is quantization.
However, the state-of-the-art only focus on employing the weight quantization
directly from a specific quantization scheme, i.e., either the post-training
quantization (PTQ) or the in-training quantization (ITQ), and do not consider
(1) quantizing other SNN parameters (e.g., neuron membrane potential), (2)
exploring different combinations of quantization approaches (i.e., quantization
schemes, precision levels, and rounding schemes), and (3) selecting the SNN
model with a good memory-accuracy trade-off at the end. Therefore, the memory
saving offered by these state-of-the-art to meet the targeted accuracy is
limited, thereby hindering processing SNNs on the resource-constrained systems
(e.g., the IoT-Edge devices). Towards this, we propose Q-SpiNN, a novel
quantization framework for memory-efficient SNNs. The key mechanisms of the
Q-SpiNN are: (1) employing quantization for different SNN parameters based on
their significance to the accuracy, (2) exploring different combinations of
quantization schemes, precision levels, and rounding schemes to find efficient
SNN model candidates, and (3) developing an algorithm that quantifies the
benefit of the memory-accuracy trade-off obtained by the candidates, and
selects the Pareto-optimal one. The experimental results show that, for the
unsupervised network, the Q-SpiNN reduces the memory footprint by ca. 4x, while
maintaining the accuracy within 1% from the baseline on the MNIST dataset. For
the supervised network, the Q-SpiNN reduces the memory by ca. 2x, while keeping
the accuracy within 2% from the baseline on the DVS-Gesture dataset.Comment: Accepted for publication at the 2021 International Joint Conference
on Neural Networks (IJCNN), July 2021, Virtual Even
Mantis: Enabling Energy-Efficient Autonomous Mobile Agents with Spiking Neural Networks
Autonomous mobile agents such as unmanned aerial vehicles (UAVs) and mobile
robots have shown huge potential for improving human productivity. These mobile
agents require low power/energy consumption to have a long lifespan since they
are usually powered by batteries. These agents also need to adapt to
changing/dynamic environments, especially when deployed in far or dangerous
locations, thus requiring efficient online learning capabilities. These
requirements can be fulfilled by employing Spiking Neural Networks (SNNs) since
SNNs offer low power/energy consumption due to sparse computations and
efficient online learning due to bio-inspired learning mechanisms. However, a
methodology is still required to employ appropriate SNN models on autonomous
mobile agents. Towards this, we propose a Mantis methodology to systematically
employ SNNs on autonomous mobile agents to enable energy-efficient processing
and adaptive capabilities in dynamic environments. The key ideas of our Mantis
include the optimization of SNN operations, the employment of a bio-plausible
online learning mechanism, and the SNN model selection. The experimental
results demonstrate that our methodology maintains high accuracy with a
significantly smaller memory footprint and energy consumption (i.e., 3.32x
memory reduction and 2.9x energy saving for an SNN model with 8-bit weights)
compared to the baseline network with 32-bit weights. In this manner, our
Mantis enables the employment of SNNs for resource- and energy-constrained
mobile agents.Comment: To appear at the 2023 International Conference on Automation,
Robotics and Applications (ICARA), February 2023, Abu Dhabi, UAE. arXiv admin
note: text overlap with arXiv:2206.0865
TopSpark: A Timestep Optimization Methodology for Energy-Efficient Spiking Neural Networks on Autonomous Mobile Agents
Autonomous mobile agents require low-power/energy-efficient machine learning
(ML) algorithms to complete their ML-based tasks while adapting to diverse
environments, as mobile agents are usually powered by batteries. These
requirements can be fulfilled by Spiking Neural Networks (SNNs) as they offer
low power/energy processing due to their sparse computations and efficient
online learning with bio-inspired learning mechanisms for adapting to different
environments. Recent works studied that the energy consumption of SNNs can be
optimized by reducing the computation time of each neuron for processing a
sequence of spikes (timestep). However, state-of-the-art techniques rely on
intensive design searches to determine fixed timestep settings for only
inference, thereby hindering the SNNs from achieving further energy efficiency
gains in both training and inference. These techniques also restrict the SNNs
from performing efficient online learning at run time. Toward this, we propose
TopSpark, a novel methodology that leverages adaptive timestep reduction to
enable energy-efficient SNN processing in both training and inference, while
keeping its accuracy close to the accuracy of SNNs without timestep reduction.
The ideas of TopSpark include: analyzing the impact of different timesteps on
the accuracy; identifying neuron parameters that have a significant impact on
accuracy in different timesteps; employing parameter enhancements that make
SNNs effectively perform learning and inference using less spiking activity;
and developing a strategy to trade-off accuracy, latency, and energy to meet
the design requirements. The results show that, TopSpark saves the SNN latency
by 3.9x as well as energy consumption by 3.5x (training) and 3.3x (inference)
on average, across different network sizes, learning rules, and workloads,
while maintaining the accuracy within 2% of SNNs without timestep reduction.Comment: To appear at the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS), October 2023, Detroit, MI, US
FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks
Spiking Neural Networks (SNNs) are gaining interest due to their event-driven
processing which potentially consumes low power/energy computations in hardware
platforms, while offering unsupervised learning capability due to the
spike-timing-dependent plasticity (STDP) rule. However, state-of-the-art SNNs
require a large memory footprint to achieve high accuracy, thereby making them
difficult to be deployed on embedded systems, for instance on battery-powered
mobile devices and IoT Edge nodes. Towards this, we propose FSpiNN, an
optimization framework for obtaining memory- and energy-efficient SNNs for
training and inference processing, with unsupervised learning capability while
maintaining accuracy. It is achieved by (1) reducing the computational
requirements of neuronal and STDP operations, (2) improving the accuracy of
STDP-based learning, (3) compressing the SNN through a fixed-point
quantization, and (4) incorporating the memory and energy requirements in the
optimization process. FSpiNN reduces the computational requirements by reducing
the number of neuronal operations, the STDP-based synaptic weight updates, and
the STDP complexity. To improve the accuracy of learning, FSpiNN employs
timestep-based synaptic weight updates, and adaptively determines the STDP
potentiation factor and the effective inhibition strength. The experimental
results show that, as compared to the state-of-the-art work, FSpiNN achieves
7.5x memory saving, and improves the energy-efficiency by 3.5x on average for
training and by 1.8x on average for inference, across MNIST and Fashion MNIST
datasets, with no accuracy loss for a network with 4900 excitatory neurons,
thereby enabling energy-efficient SNNs for edge devices/embedded systems.Comment: To appear at the IEEE Transactions on Computer-Aided Design of
Integrated Circuits and Systems (IEEE-TCAD), as part of the ESWEEK-TCAD
Special Issue, September 202
Noise and Bandwidth Consideration in Designing Op-Amp Based Transimpedance Amplifier for VLC
In a visible light communication (VLC) system, there are many modules involved. One of the important modules is Transimpedance Amplifier (TIA) that resides in the analog front-end receiver (Rx-AFE). TIA is responsible for performing signal conversion from current signal, which is provided from the photodiode (PD) to voltage signal. It is the reason why the TIA should be operating in low noise condition and wide bandwidth of frequency. These will enable a flexible coverage of the VLC system in performing its signal processing. Hence, in this research, we provide considerations of the noise and frequency bandwidth analysis in designing TIA to cope with the required design specification of a VLC system
SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM
Spiking Neural Networks (SNNs) have the potential for achieving low energy
consumption due to their biologically sparse computation. Several studies have
shown that the off-chip memory (DRAM) accesses are the most energy-consuming
operations in SNN processing. However, state-of-the-art in SNN systems do not
optimize the DRAM energy-per-access, thereby hindering achieving high
energy-efficiency. To substantially minimize the DRAM energy-per-access, a key
knob is to reduce the DRAM supply voltage but this may lead to DRAM errors
(i.e., the so-called approximate DRAM). Towards this, we propose SparkXD, a
novel framework that provides a comprehensive conjoint solution for resilient
and energy-efficient SNN inference using low-power DRAMs subjected to
voltage-induced errors. The key mechanisms of SparkXD are: (1) improving the
SNN error tolerance through fault-aware training that considers bit errors from
approximate DRAM, (2) analyzing the error tolerance of the improved SNN model
to find the maximum tolerable bit error rate (BER) that meets the targeted
accuracy constraint, and (3) energy-efficient DRAM data mapping for the
resilient SNN model that maps the weights in the appropriate DRAM location to
minimize the DRAM access energy. Through these mechanisms, SparkXD mitigates
the negative impact of DRAM (approximation) errors, and provides the required
accuracy. The experimental results show that, for a target accuracy within 1%
of the baseline design (i.e., SNN without DRAM errors), SparkXD reduces the
DRAM energy by ca. 40% on average across different network sizes.Comment: To appear at the 58th IEEE/ACM Design Automation Conference (DAC),
December 2021, San Francisco, CA, US
EnforceSNN: Enabling Resilient and Energy-Efficient Spiking Neural Network Inference considering Approximate DRAMs for Embedded Systems
Spiking Neural Networks (SNNs) have shown capabilities of achieving high
accuracy under unsupervised settings and low operational power/energy due to
their bio-plausible computations. Previous studies identified that DRAM-based
off-chip memory accesses dominate the energy consumption of SNN processing.
However, state-of-the-art works do not optimize the DRAM energy-per-access,
thereby hindering the SNN-based systems from achieving further energy
efficiency gains. To substantially reduce the DRAM energy-per-access, an
effective solution is to decrease the DRAM supply voltage, but it may lead to
errors in DRAM cells (i.e., so-called approximate DRAM). Towards this, we
propose \textit{EnforceSNN}, a novel design framework that provides a solution
for resilient and energy-efficient SNN inference using reduced-voltage DRAM for
embedded systems. The key mechanisms of our EnforceSNN are: (1) employing
quantized weights to reduce the DRAM access energy; (2) devising an efficient
DRAM mapping policy to minimize the DRAM energy-per-access; (3) analyzing the
SNN error tolerance to understand its accuracy profile considering different
bit error rate (BER) values; (4) leveraging the information for developing an
efficient fault-aware training (FAT) that considers different BER values and
bit error locations in DRAM to improve the SNN error tolerance; and (5)
developing an algorithm to select the SNN model that offers good trade-offs
among accuracy, memory, and energy consumption. The experimental results show
that our EnforceSNN maintains the accuracy (i.e., no accuracy loss for BER
less-or-equal 10^-3) as compared to the baseline SNN with accurate DRAM, while
achieving up to 84.9\% of DRAM energy saving and up to 4.1x speed-up of DRAM
data throughput across different network sizes.Comment: Accepted for publication at Frontiers in Neuroscience - Section
Neuromorphic Engineerin
RescueSNN: Enabling Reliable Executions on Spiking Neural Network Accelerators under Permanent Faults
To maximize the performance and energy efficiency of Spiking Neural Network
(SNN) processing on resource-constrained embedded systems, specialized hardware
accelerators/chips are employed. However, these SNN chips may suffer from
permanent faults which can affect the functionality of weight memory and neuron
behavior, thereby causing potentially significant accuracy degradation and
system malfunctioning. Such permanent faults may come from manufacturing
defects during the fabrication process, and/or from device/transistor damages
(e.g., due to wear out) during the run-time operation. However, the impact of
permanent faults in SNN chips and the respective mitigation techniques have not
been thoroughly investigated yet. Toward this, we propose RescueSNN, a novel
methodology to mitigate permanent faults in the compute engine of SNN chips
without requiring additional retraining, thereby significantly cutting down the
design time and retraining costs, while maintaining the throughput and quality.
The key ideas of our RescueSNN methodology are (1) analyzing the
characteristics of SNN under permanent faults; (2) leveraging this analysis to
improve the SNN fault-tolerance through effective fault-aware mapping (FAM);
and (3) devising lightweight hardware enhancements to support FAM. Our FAM
technique leverages the fault map of SNN compute engine for (i) minimizing
weight corruption when mapping weight bits on the faulty memory cells, and (ii)
selectively employing faulty neurons that do not cause significant accuracy
degradation to maintain accuracy and throughput, while considering the SNN
operations and processing dataflow. The experimental results show that our
RescueSNN improves accuracy by up to 80% while maintaining the throughput
reduction below 25% in high fault rate (e.g., 0.5 of the potential fault
locations), as compared to running SNNs on the faulty chip without mitigation.Comment: Accepted for publication at Frontiers in Neuroscience - Section
Neuromorphic Engineerin