Case Studies

Case Study: Harnessing Computer Vision and Machine Learning for Real-World Products

The buzz around artificial intelligence, computer vision and machine learning intensifies on a daily basis: There are a dizzying number of new processors, algorithms and tools for computer vision and machine learning. Investment and acquisition activity around AI companies is furious. Announcements of new AI-based applications and products are non-stop. Competition for engineering talent is fierce. All this creates challenges for product designers and application developers who seek to Read more...

Case Study: Engineering Algorithms for Efficiency and Effectiveness

Algorithms are the essence of embedded applications; they are the mathematical processes that transform data in useful ways. They’re also often computationally demanding. When designing a new product, companies often need to assess whether an algorithm will fit within their cost and power consumption targets. Sometimes, an algorithm won’t fit in its initial form. Most algorithms can be formulated in many different ways and different formulations will be more or less efficient on different Read more...

Case Study: The Path to Credible, Convincing Benchmarks

Benchmarks are an important resource for system and processor designers alike. System designers need good benchmarks to understand how a processor will meet the requirements of their applications and to compare processors accurately. Processor designers need good benchmarks to assess how their processors will perform on their target applications and to prove their processors' capabilities to customers. But before determining what a good benchmark is, it’s best to ask what you want the Read more...

Case Study: Unlocking the Capabilities of Today’s Complex SoCs for Vision

As SoCs become more complex and specialized, incorporating numerous and varied processor cores and dedicated accelerators, it has become more and more difficult to program them. This is particularly true of chips targeting vision-based applications. To meet the performance demands and high data rates of vision applications, vendors are designing heterogeneous devices that incorporate different classes of processors—CPUs, DSPs, GPUs, FPGAs, and special-purpose engines. Programming each of these Read more...

Case Study: Making the Right Decisions on Using Deep Learning

Ten years ago, no one would have expected that neural networks would deliver such impressive results on computer vision problems. Since 2012, when AlexNet, a deep convolutional neural network, produced a significantly lower error rate on the ImageNet Large Scale Visual Recognition Challenge than traditional feature extraction approaches, investigation of neural network approaches for visual understanding has intensified. These efforts culminated in 2015 when a deep residual network (ResNet) Read more...

Case Study: Today’s Demanding Algorithms Complicate Processor Selection Decisions

As system designers race to make IoT and edge devices more capable, they are incorporating increasingly complex and demanding algorithms. Cameras and microphones are now the eyes and ears of systems that help us drive our cars, maintain the safety of our homes, diagnose health issues, and much more. Processor vendors, seeking to meet escalating requirements of processing sensor data at the edge, are designing new heterogeneous devices that integrate CPU cores, DSPs, GPUs, and other specialized Read more...

Case Study: Bringing the Power of Deep Learning to Embedded Processors

As the number of IoT devices increases, so does the need for intelligence at the edge—intelligence that will enable a device to acquire insights from its surroundings and make decisions in real-time. Particularly for devices such as drones, personal robots, and autonomous vehicles, real-time decision-making capability is a must. Machine learning approaches, such as deep and convolutional neural networks (DNNs and CNNs) are proving to be the most accurate means for object detection and Read more...

Case Study: Using Deep Learning Techniques for Commercial Product Success

Here at BDTI, we’re working to apply deep learning techniques to a wide range of applications. Deep learning can be extremely effective—if there’s the right combination of data and processing power. The challenge to success lies in understanding how much data is sufficient and how to process it efficiently. This is where BDTI’s expertise in algorithms and architectures delivers value to our customers. Recently, BDTI was engaged to create a convolutional neural network (CNN) to classify items Read more...

Case Study: Hands-On Experience Delivers Insights Into Technologies and Trends for Deep Learning

One of the most interesting resources for those interested in the rapidly growing field of deep learning is the "Cognitive Computing Startup List," compiled by Chris Rowan of Cognite Ventures. As of February 21, Chris had identified 275 companies that are, in his words, "the most focused, the most active and the most innovative…." These were culled from various lists of companies that tout artificial intelligence and deep learning. Such companies number in the thousands—the sheer number is a Read more...

Case Study: Deep Understanding of Processor Architectures and Computer Vision Algorithms is Key to a Breakthrough Product

Computer vision promises to be the key to the next set of "killer apps"—computer vision-enabled apps that will leverage artificial intelligence to help keep us safer and healthier. But computer vision algorithms are compute- and power-intensive, and need to process large amounts of data. These barriers have limited their use to enterprise, line-powered devices and cloud-assisted mobile devices. The implementation of computer vision algorithms on mobile processors, where compute resources are Read more...