3,158 research outputs found
A Hardware Implementation of Artificial Neural Network Using Field Programmable Gate Arrays
An artificial neural network algorithm is implemented using a field
programmable gate array hardware. One hidden layer is used in the feed-forward
neural network structure in order to discriminate one class of patterns from
the other class in real time. With five 8-bit input patterns, six hidden nodes,
and one 8-bit output, the implemented hardware neural network makes decision on
a set of input patterns in 11 clocks and the result is identical to what to
expect from off-line computation. This implementation may be used in level 1
hardware triggers in high energy physics experimentsComment: 13 pages, 4 figures, submitted to Nucl. Instr. Meth.
Using LSTM recurrent neural networks for monitoring the LHC superconducting magnets
The superconducting LHC magnets are coupled with an electronic monitoring
system which records and analyses voltage time series reflecting their
performance. A currently used system is based on a range of preprogrammed
triggers which launches protection procedures when a misbehavior of the magnets
is detected. All the procedures used in the protection equipment were designed
and implemented according to known working scenarios of the system and are
updated and monitored by human operators.
This paper proposes a novel approach to monitoring and fault protection of
the Large Hadron Collider (LHC) superconducting magnets which employs
state-of-the-art Deep Learning algorithms. Consequently, the authors of the
paper decided to examine the performance of LSTM recurrent neural networks for
modeling of voltage time series of the magnets. In order to address this
challenging task different network architectures and hyper-parameters were used
to achieve the best possible performance of the solution. The regression
results were measured in terms of RMSE for different number of future steps and
history length taken into account for the prediction. The best result of
RMSE=0.00104 was obtained for a network of 128 LSTM cells within the internal
layer and 16 steps history buffer
DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car
We present DeepPicar, a low-cost deep neural network based autonomous car
platform. DeepPicar is a small scale replication of a real self-driving car
called DAVE-2 by NVIDIA. DAVE-2 uses a deep convolutional neural network (CNN),
which takes images from a front-facing camera as input and produces car
steering angles as output. DeepPicar uses the same network architecture---9
layers, 27 million connections and 250K parameters---and can drive itself in
real-time using a web camera and a Raspberry Pi 3 quad-core platform. Using
DeepPicar, we analyze the Pi 3's computing capabilities to support end-to-end
deep learning based real-time control of autonomous vehicles. We also
systematically compare other contemporary embedded computing platforms using
the DeepPicar's CNN-based real-time control workload. We find that all tested
platforms, including the Pi 3, are capable of supporting the CNN-based
real-time control, from 20 Hz up to 100 Hz, depending on hardware platform.
However, we find that shared resource contention remains an important issue
that must be considered in applying CNN models on shared memory based embedded
computing platforms; we observe up to 11.6X execution time increase in the CNN
based control loop due to shared resource contention. To protect the CNN
workload, we also evaluate state-of-the-art cache partitioning and memory
bandwidth throttling techniques on the Pi 3. We find that cache partitioning is
ineffective, while memory bandwidth throttling is an effective solution.Comment: To be published as a conference paper at RTCSA 201
A committee machine gas identification system based on dynamically reconfigurable FPGA
This paper proposes a gas identification system based on the committee machine (CM) classifier, which combines various gas identification algorithms, to obtain a unified decision with improved accuracy. The CM combines five different classifiers: K nearest neighbors (KNNs), multilayer perceptron (MLP), radial basis function (RBF), Gaussian mixture model (GMM), and probabilistic principal component analysis (PPCA). Experiments on real sensors' data proved the effectiveness of our system with an improved accuracy over individual classifiers. Due to the computationally intensive nature of CM, its implementation requires significant hardware resources. In order to overcome this problem, we propose a novel time multiplexing hardware implementation using a dynamically reconfigurable field programmable gate array (FPGA) platform. The processing is divided into three stages: sampling and preprocessing, pattern recognition, and decision stage. Dynamically reconfigurable FPGA technique is used to implement the system in a sequential manner, thus using limited hardware resources of the FPGA chip. The system is successfully tested for combustible gas identification application using our in-house tin-oxide gas sensors
Evaluating Built-in ECC of FPGA on-chip Memories for the Mitigation of Undervolting Faults
Voltage underscaling below the nominal level is an effective solution for
improving energy efficiency in digital circuits, e.g., Field Programmable Gate
Arrays (FPGAs). However, further undervolting below a safe voltage level and
without accompanying frequency scaling leads to timing related faults,
potentially undermining the energy savings. Through experimental voltage
underscaling studies on commercial FPGAs, we observed that the rate of these
faults exponentially increases for on-chip memories, or Block RAMs (BRAMs). To
mitigate these faults, we evaluated the efficiency of the built-in
Error-Correction Code (ECC) and observed that more than 90% of the faults are
correctable and further 7% are detectable (but not correctable). This
efficiency is the result of the single-bit type of these faults, which are then
effectively covered by the Single-Error Correction and Double-Error Detection
(SECDED) design of the built-in ECC. Finally, motivated by the above
experimental observations, we evaluated an FPGA-based Neural Network (NN)
accelerator under low-voltage operations, while built-in ECC is leveraged to
mitigate undervolting faults and thus, prevent NN significant accuracy loss. In
consequence, we achieve 40% of the BRAM power saving through undervolting below
the minimum safe voltage level, with a negligible NN accuracy loss, thanks to
the substantial fault coverage by the built-in ECC.Comment: 6 pages, 2 figure
Personalized Health Monitoring Using Evolvable Block-based Neural Networks
This dissertation presents personalized health monitoring using evolvable block-based neural networks. Personalized health monitoring plays an increasingly important role in modern society as the population enjoys longer life. Personalization in health monitoring considers physiological variations brought by temporal, personal or environmental differences, and demands solutions capable to reconfigure and adapt to specific requirements. Block-based neural networks (BbNNs) consist of 2-D arrays of modular basic blocks that can be easily implemented using reconfigurable digital hardware such as field programmable gate arrays (FPGAs) that allow on-line partial reorganization. The modular structure of BbNNs enables easy expansion in size by adding more blocks. A computationally efficient evolutionary algorithm is developed that simultaneously optimizes structure and weights of BbNNs. This evolutionary algorithm increases optimization speed by integrating a local search operator. An adaptive rate update scheme removing manual tuning of operator rates enhances the fitness trend compared to pre-determined fixed rates. A fitness scaling with generalized disruptive pressure reduces the possibility of premature convergence. The BbNN platform promises an evolvable solution that changes structures and parameters for personalized health monitoring. A BbNN evolved with the proposed evolutionary algorithm using the Hermite transform coefficients and a time interval between two neighboring R peaks of ECG signal, provides a patient-specific ECG heartbeat classification system. Experimental results using the MIT-BIH Arrhythmia database demonstrate a potential for significant performance enhancements over other major techniques
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