FPGA Implementation of Hand-written Number Recognition Based on CNN

Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2-dimensional FIR filters that convolve the input signal with the learned feature maps.  For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a hand-written number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%

an automatic aw som vhdl ip core generator

In this paper, the authors present a MATLAB IP generator for hardware accelerators of All-Winner Self-Organizing Maps (AW-SOM). AW-SOM is a modified version of Kohonen's Self Organizing Maps (SOM) algorithm, which is one of the most used Machine Learning algorithms for data clustering, and vector quantization. The architecture of the AW-SOM method is meant for hardware implementations, and its main feature is a processing speed almost independent to the number of neurons since each of them is processed in a parallel way; the parallelization can be easily exploited by hardware custom hardware designs. The IP generator is built-in MATLAB and provides the user with the possibility to design a custom and efficient hardware accelerator. Several settings can be set such as the number of features and the number of neurons. The target language is the VHSIC Hardware Description Language (VHDL). The generated IP cores can be used for the training of the model and a built-in function of the software can also check the clustering performances using its inference capabilities. The accelerators produced by the software have been also characterized in terms of max frequency, hardware resources, and power consumption. The authors performed the hardware implementations on a XILINX Virtex 7 xc7vx690t FPGA

A Feature Extractor IC for Acoustic Emission Non-destructive Testing

In this paper, we present the design and the implementation of a digital Application Specific Integrated Circuit (ASIC) for Acoustic Emission (AE) non-destructive testing. The AE non-destructive testing method is a diagnostic method used to detect faults in mechanically loaded structures and components. If a structure is subjected to mechanical load or stress, the presence of structural discontinuities releases energy in the form of acoustic emissions through the constituting material. The analysis of these acoustic emissions can be used to determine the presence of faults in several structures. The proposed circuit has been designed for IoT (Internet of Things) applications, and it can be used to simplify the existing procedures adopted for structural integrity verifications of pressurized metal tanks that, in some countries, they are based on periodic checks. The proposed ASIC is provided of Digital Signal Processing (DSP) capabilities for the extraction of the main four parameters used in the AE analysis that are the energy of the signal, the duration of the event, the number of the crossing of a certain threshold and finally the maximum value reached by the AE signal. The circuit is provided of an SPI interface capable of sending and receiving data to/from wireless transceivers to share information on the web. The DSP circuit has been coded in VHDL and synthesized in 90 nm technology using Synopsys. The circuit has been characterized in terms of area, speed, and power consumption. Experimental results show that the proposed circuit presents very low power consumption properties and low area requirements

fpga implementation of hand written number recognition based on cnn

Convolutional Neural Networks (CNNs) are the state-of-the-art in computer vision for different purposes such as image and video classification, recommender systems and natural language processing. The connectivity pattern between CNNs neurons is inspired by the structure of the animal visual cortex. In order to allow the processing, they are realized with multiple parallel 2-dimensional FIR filters that convolve the input signal with the learned feature maps. For this reason, a CNN implementation requires highly parallel computations that cannot be achieved using traditional general-purpose processors, which is why they benefit from a very significant speed-up when mapped and run on Field Programmable Gate Arrays (FPGAs). This is because FPGAs offer the capability to design full customizable hardware architectures, providing high flexibility and the availability of hundreds to thousands of on-chip Digital Signal Processing (DSP) blocks. This paper presents an FPGA implementation of a hand-written number recognition system based on CNN. The system has been characterized in terms of classification accuracy, area, speed, and power consumption. The neural network was implemented on a Xilinx XC7A100T FPGA, and it uses 29.69% of Slice LUTs, 4.42% of slice registers and 52.50% block RAMs. We designed the system using a 9-bit representation that allows for avoiding the use of DSP. For this reason, multipliers are implemented using LUTs. The proposed architecture can be easily scaled on different FPGA devices thank its regularity. CNN can reach a classification accuracy of 90%

Comparison between Trigonometric, and traditional DDS, in 90 nm technology

The Direct Digital frequency Synthesizer (DDS) is an architecture largely used for the generation of numeric sine and/or cosine waveforms in different applications. In this work, authors compare two different DDS architectures: the traditional architecture, based on the exploitation of quarter wave symmetry, and the Symon’s DDS (trigonometric DDS) presented in 2002. The two layout configurations have been implemented in 90 nm technology and compared in terms of area, speed and power consumption. Comparisons have been performed in terms of circuital complexity on architectures having the same Spurious Free Dynamic Range (SFDR) and phase resolution. Experiments show that the trigonometric architecture is very efficient in terms of area

Medication-related osteonecrosis of the jaw

In 2014, the nomenclature of bisphosphonate-related osteonecrosis of the jaw (BRONJ) was changed in medication-related osteonecrosis of the jaw (MRONJ) to include osteonecrosis of the jaw caused by non-bisphosphonates (BPs) drugs. MRONJs are a rare drug adverse reaction associated with BPs and other antiresorptive (denosumab) and antiangiogenetic therapies. MRONJ pathophysiology is not completely elucidated, and three risk factors should be considered: Local factors, underlying disease and kind of medication. MRONJ aff ects considerably patient’s quality of life, so it is important to know pathology and risk factor in order to prevent or treat immediately the disease. Various BRONJ staging systems are used by clinicians: In 2006 Ruggero at al. proposed a clinical staging system with three diff erent levels based on signs and symthoms; in 2009 American Association of Oral and Maxillofacial Surgeons implemented it with Stage 0. Marx in 2007 was the only one who divided the stages on the basis of the lesion’s size. Bedogni in 2012 proposed a clinical-radiological staging system. The aim of this review is to summarize the current diagnosis, prevention and treatment strategies

Survival of short dental implants ≤7 mm: A review

The first long-term successful outcome of short dental implants was demonstrated by Frieberg et al. in 1991, however, the definition of “short” implants is still controversial and without uniform consensus nowadays. The specific aim of this review was to evaluate and to compare cumulative survival rate (CSR) of short dental implants of the two groups. The survival rate of short dental implants was the primary outcome variable to be extracted and analyzed. An electronic search was conducted through the Medline (PubMed) database of the National Library of Medicine, and EMBASE to find all relevant articles published between January 1, 1990, and April 30, 2015. The electronic search identified 347 publications, which were all carefully screened by title and abstract. About 65 articles qualified for a thorough full-text analysis: 35 studies were excluded because CSR% was not calculable. Finally, 30 studies with relevant data on CSR were selected to be included in this review. Articles were divided into two groups: All relevant articles published between 1991 and 2000 as Group 1 and between 2001 and 2015 as Group 2. In Group 1 CSR was 83.53% ± 19.46%, a considerable statistically significant difference compared to 93.65% ± 7.94% of Group 2. This review further identified the causes of failure: In Group 1 the majority of short implant failures occurred early, within the first 4 months, for an insufficient quantity of bone tissue. In Group 2, causes of early failures considered were low bone quality while prosthetic reasons were responsible for delayed failures

Case Report: Severe Rhabdomyolysis and Multiorgan Failure After ChAdOx1 nCoV-19 Vaccination

Background: Severe skeletal muscle damage has been recently reported in patients with SARS-CoV-2 infection and as a rare vaccination complication. Case summary: On Apr 28, 2021 a 68-year-old man who was previously healthy presented with an extremely severe rhabdomyolysis that occurred nine days following the first dose of SARS-CoV-2 ChAdOx1 nCov-19 vaccination. He had no risk factors, and denied any further assumption of drugs except for fermented red rice, and berberine supplement. The clinical scenario was complicated by a multi organ failure involving bone marrow, liver, lung, and kidney. For the rapid increase of the inflammatory markers, a cytokine storm was suspected and multi-target biologic immunosuppressive therapy was started, consisting of steroids, anakinra, and eculizumab, which was initially successful resulting in close to normal values of creatine phosphokinase after 17 days of treatment. Unfortunately, 48 days after the vaccination an accelerated phase of deterioration, characterized by severe multi-lineage cytopenia, untreatable hypotensive shock, hypoglycemia, and dramatic increase of procalcitonin (PCT), led to patient death. Conclusion: Physicians should be aware that severe and fatal rhabdomyolysis may occur after SARS-CoV2 vaccine administration