A PROGRAMMABLE ACTIVE MEMORY IMPLEMENTATION OF A NEURAL NETWORK FOR SECOND LEVEL TRIGGERING IN ATLAS

1995 ◽  
Vol 06 (04) ◽  
pp. 561-566 ◽  
Author(s):  
LARS LUNDHEIM ◽  
IOSIF LEGRAND ◽  
LAURENT MOLL
Keyword(s):  
Regions Of Interest ◽  
Neural Net ◽  
Particle Type ◽  
Reconfigurable Processor ◽  
Gate Arrays ◽  
Active Memory ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Input Variables

In the second level triggering for ATLAS “Regions of Interest” (RoIs) are defined in (etha, phi) corresponding to possibly interesting particles. For each RoI physically meaningful parameters are extracted for each subdetector. Based on these parameters a classification of the particle type is made. A feed-forward neural net with 12 input variables, a 6-node intermediate layer, and 4 output nodes has earlier been suggested for this task. The reported work consists of an implementation of this neural net using a DECPeRLe-1, a Programmable Active Memory (PAM). This is a reconfigurable processor based on Field Programmable Gate Arrays (FPGAs), which has also been used for real-time implementation of feature extraction algorithms for second level triggering. The implementation is pipelined, runs with a clock of 25 MHz, and uses 0.64 microseconds for one particle classification. Integer arithmetic is used, and the performance is comparable to a floating point version.

scholarly journals Pattern Classification of Fabric Defects Using a Probabilistic Neural Network and Its Hardware Implementation using the Field Programmable Gate Array System

2017 ◽  
Vol 25 (0) ◽  
pp. 42-48 ◽  
Author(s):  
Abul Hasnat ◽  
Anindya Ghosh ◽  
Amina Khatun ◽  
Santanu Halder
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Probabilistic Neural Network ◽  
Clock Period ◽  
Data Set ◽  
Gate Arrays ◽  
Hardware System ◽  
Field Programmable ◽  
Programmable Gate Arrays

This study proposes a fabric defect classification system using a Probabilistic Neural Network (PNN) and its hardware implementation using a Field Programmable Gate Arrays (FPGA) based system. The PNN classifier achieves an accuracy of 98 ± 2% for the test data set, whereas the FPGA based hardware system of the PNN classifier realises about 94±2% testing accuracy. The FPGA system operates as fast as 50.777 MHz, corresponding to a clock period of 19.694 ns.

scholarly journals Implementation of Multispectral Image Classification on a Remote Adaptive Computer

VLSI Design ◽  
2000 ◽  
Vol 10 (3) ◽  
pp. 307-319
Author(s):  
Marco A. Figueiredo ◽  
Clay S. Gloster ◽  
Mark Stephens ◽  
Corey A. Graves ◽  
Mouna Nakkar
Keyword(s):  
Probabilistic Neural Network ◽  
General Purpose ◽  
Multispectral Images ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
General Purpose Computer ◽  
Server Application ◽  
Purpose Computer

As the demand for higher performance computers for the processing of remote sensing science algorithms increases, the need to investigate new computing paradigms is justified. Field Programmable Gate Arrays enable the implementation of algorithms at the hardware gate level, leading to orders of magnitude performance increase over microprocessor based systems. The automatic classification of spaceborne multispectral images is an example of a computation intensive application that can benefit from implementation on an FPGA-based custom computing machine (adaptive or reconfigurable computer). A probabilistic neural network is used here to classify pixels of a multispectral LANDSAT-2 image. The implementation described utilizes Java client/server application programs to access the adaptive computer from a remote site. Results verify that a remote hardware version of the algorithm (implemented on an adaptive computer) is significantly faster than a local software version of the same algorithm (implemented on a typical general-purpose computer).

A Metallic CNT Tolerant Design Methodology for Carbon Nanotube-Based Programmable Gate Arrays

2015 ◽  
Vol 25 (02) ◽  
pp. 1650016 ◽  
Author(s):  
Alireza Tajary ◽  
Behnam Ghavami
Keyword(s):  
Carbon Nanotube ◽  
Fault Simulation ◽  
Cmos Technology ◽  
Lookup Table ◽  
Gate Arrays ◽  
Multi Stage ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
Metallic Cnts ◽  
Input Variables

Carbon nanotube field effect transistor (CNFET) is one of the promising technologies as a replacement for current CMOS technology due to its excellent electronic properties. CNFETs can be fabricated in regular structures, making them ideal for creating the repetitive architectures found in field programmable gate arrays (FPGAs). However, CNFETs face some fabrication challenges. The unwanted metallic carbon nanotubes (CNTs) are one of the major challenges in using CNFET technology for FPGAs. In this paper, we take the advantage of FPGAs programmability allowing reconfiguration around the metallic CNTs to tolerate this defect. We demonstrate a multi-stage solution to the metallic CNT problem in CNFET-based FPGAs that does not require any metallic nanotube removal of any kind. The proposed methodology consists of four consecutive stages in logic mapping process: (i) reordering of input variables, (ii) inputs complementing, (iii) adding inputs redundancy to basic logic element (BLE) and (iv) BLE lookup table (LUT) splitting. A fault simulation tool is designed to work closely with VPR, an academic FPGA CAD tool, to provide the investigation of metallic CNTs effects on CNFET-based FPGAs. Experimental results show that the proposed method can successfully map all logical nets at a cost of [Formula: see text] area overhead if the fraction of metallic CNTs is reduced to 30%.

scholarly journals FPGA-Based Controller for a Hybrid Grid-Connected PV/Wind/Battery Power System with AC Load

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2108
Author(s):  
Mohamed Yassine Allani ◽  
Jamel Riahi ◽  
Silvano Vergura ◽  
Abdelkader Mami
Keyword(s):  
Fuzzy Logic ◽  
Hybrid System ◽  
High Voltage ◽  
Field Programmable Gate Arrays ◽  
Energy System ◽  
Maximum Power ◽  
Hybrid Energy ◽  
Gate Arrays ◽  
Field Programmable ◽  
Programmable Gate Arrays

The development and optimization of a hybrid system composed of photovoltaic panels, wind turbines, converters, and batteries connected to the grid, is first presented. To generate the maximum power, two maximum power point tracker controllers based on fuzzy logic are required and a battery controller is used for the regulation of the DC voltage. When the power source varies, a high-voltage supply is incorporated (high gain DC-DC converter controlled by fuzzy logic) to boost the 24 V provided by the DC bus to the inverter voltage of about 400 V and to reduce energy losses to maximize the system performance. The inverter and the LCL filter allow for the integration of this hybrid system with AC loads and the grid. Moreover, a hardware solution for the field programmable gate arrays-based implementation of the controllers is proposed. The combination of these controllers was synthesized using the Integrated Synthesis Environment Design Suite software (Version: 14.7, City: Tunis, Country: Tunisia) and was successfully implemented on Field Programmable Gate Arrays Spartan 3E. The innovative design provides a suitable architecture based on power converters and control strategies that are dedicated to the proposed hybrid system to ensure system reliability. This implementation can provide a high level of flexibility that can facilitate the upgrade of a control system by simply updating or modifying the proposed algorithm running on the field programmable gate arrays board. The simulation results, using Matlab/Simulink (Version: 2016b, City: Tunis, Country: Tunisia, verify the efficiency of the proposed solution when the environmental conditions change. This study focused on the development and optimization of an electrical system control strategy to manage the produced energy and to coordinate the performance of the hybrid energy system. The paper proposes a combined photovoltaic and wind energy system, supported by a battery acting as an energy storage system. In addition, a bi-directional converter charges/discharges the battery, while a high-voltage gain converter connects them to the DC bus. The use of a battery is useful to compensate for the mismatch between the power demanded by the load and the power generated by the hybrid energy systems. The proposed field programmable gate arrays (FPGA)-based controllers ensure a fast time response by making control executable in real time.

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