How to Mitigate the Chip Shortage Crisis with Software
We have an insatiable demand for technology in all aspects of our lives. We need technology to make our work more efficient, our lives safer and healthier, and our lifestyles more comfortable. Central to the technology are the semiconductor chips that are responsible for the computations that make our electronics smarter. Experts have been warning ...
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The Adaptive AI™ Approach: Run Deep Neural Networks with Optimal Performance
If you are an AI engineer dissatisfied with the speed or power consumption of your neural network inferences and believe there are no solutions available for better performance….then keep reading! I’d like to introduce a new way to run your deep neural networks at their most optimal point. It is dynamic, context-aware, and flexible. It ...
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How Latent AI’s LEIP SDK Improves Deep Learning Inference Efficiency
Our Latent AI Efficient Inference Platform (LEIP) is an SDK for deep learning training and inference. LEIP provides APIs to train, quantize, and deploy edge AI models from many deep learning frameworks. It then generates optimized runtime engines deployable in embedded devices and infrastructure edge devices. Latent AI’s LEIP SDK enables you to deploy deep ...
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The Quantization Myth
Demystifying Quantization Do you need high bit-precision to operate neural networks? More specifically, do you need a high bit-precision processor to run neural network inferences in the same level of fidelity that you’ve trained them? These concepts seem to be popular myths but are not true. A large part of the confusion around these myths ...
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Scaling Edge AI in a Data Driven World, Part Two
The Essence of Edge AI Many applications today need edge computing. However, due to privacy issues, the need for real-time responses, and lack of network connectivity, applications are often compromised. A similar position supporting edge computing comes from the argument around network scalability. The cost and latency for network backhaul can be problematic, especially when ...
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Scaling Edge AI in a Data-Driven World, Part One
Scaling Edge AI in a Data-Driven World, Part One Drowning in Data We live in a data-driven world where IoT devices are always on, always tracking, and always monitoring. Being perpetually connected also means that we’re producing mind-boggling amounts of data. In fact, IDC Research predicts by 2020 we will be generating 44 trillion gigabytes ...
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The Era of Commercializing AI, Part Two
Supporting Efficient Workflow for Commercializing AI At the early stage of AI commercialization, we are faced with the “dark ages” of tools, infrastructure, and approaches. There are many analogies that we can draw upon to understand where we truly are at this moment. For example, in the early days of the Internet, everyone was building ...
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The Era of Commercializing AI, Part One
Beyond the Valley of Innovation Death Rapid success in deep learning algorithms has spawned new applications in video analytics, speech processing, and natural language processing. New smart products with compelling user experience, driven by the power of AI, can create safer human environments, allows us to work more efficiently, and helps to make communication easier. ...
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The Next Wave in AI and Machine Learning: Adaptive AI at the Edge
The accessibility of high-end GPUs and rapid development of machine learning software frameworks (Tensorflow, Pytorch, Caffe) have enabled recent success in the deployment of deep learning applications on the cloud. Applications in video analytics, audio processing, natural language processing (NLP) are becoming popular as consumers benefit from the new user experience, driven by the power ...
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