Solar panel defect detection using Vision Intelligence Systems
Lean, green and renewable energy are some of the buzzwords of the last decade. Beyond the buzz, there’s the real grassroots level transformation that their adoption is bringing across our beautiful blue planet, critical to the survival of mankind as we know it.
A new lightweight CNN model for Automatic Speech Command Recognition on Microcontrollers
The need for Automatic Speech Command Recognition (ASCR / ASR) on IoT devices is gaining traction because of the increased interest in non-touch-based applications. This article introduces a new lightweight convolutional neural network (CNN) for ASR on microcontrollers.
Ignitarium opens new office in Japan
Ignitarium is excited to announce the opening of its new office at Yokohama, Japan, strengthening its presence in the APAC market while it gears up for its fast-approaching 10th anniversary. This strategic expansion is a further step in Ignitarium’s journey as a key player in the Semiconductor, Embedded and AI space with a global footprint spanning APAC, North America and Europe. The company has its headquarters at Bengaluru, India.
AI-based Automatic Contour-following of Roads and Canals
Roads and water canals have historically been opening up channels of commerce in economies across the world and continue to do so even today. The socio-economic development of many countries has been linked to the development and maintenance of infrastructure.
Deep Learning based Computer Vision System for Automated Tyre Defect Detection
Business photo created by freepik.com As the global manufacturing industry faces the pressures of bringing to market multiple variants of the highest quality products in the shortest possible time, a shift towards AI-driven automation across all functions has become inevitable. In quality inspection, AI-driven computer vision systems are already enabling the streamlining of the production …
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Vision AI powered Automatic Number Plate Recognition (ANPR) for Japan
Automatic Number Plate Recognition (ANPR), also called Automatic License Plate Recognition (ALPR), refers to an accurate image processing system used for detecting and reading vehicle number plates. It uses an Optical Character Recognition (OCR) component on images of number plates that are captured at high speed, to detect individual characters on the number plate and …
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Automotive Hardware Functional Safety (FuSa) Features: ISO 26262
In all critical applications like aircrafts, medical equipment and automobiles, there is a requirement for the systems to be reliable and safe. These requirements are important since human lives are at stake, which have led to the development of safety standards in various industries. The basic idea behind functional safety is that the overall system …
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Ignitarium joins Arm’s Functional Safety Partnership Program
Ignitarium is proud to announce that it is now a member of the Arm Functional Safety Partnership Program. Over the years, Ignitarium has built expertise on the Arm family of processors specifically on Automotive SoC. The team provides value-added services to partners and customers in the areas of Functional safety SDK, supporting them in achieving …
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A cost-effective debug solution using OpenOCD for hardware platforms
In today’s world, hardware platforms are growing in complexity and reducing the time to market for these platforms is key to capturing the market segment. Gone are the days when the hardware was considered as a critical piece; customers now expect the board to be delivered as a package containing world-class hardware infrastructure with reliable …
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Batch Normalization: A different perspective from Quantized Inference Model
The benefits of Batch Normalization in training are well known for the reduction of internal covariate shift and hence optimizing the training to converge faster. This article tries to bring in a different perspective, where the quantization loss is recovered with the help of Batch Normalization layer, thus retaining the accuracy of the model. The article also gives a simplified implementation of Batch Normalization to reduce the load on edge devices which generally will have constraints on computation of neural network models.
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