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The Growing Promise of On-Device AI: Exploring New Horizons in Enterprise and Consumer Technology

Artificial Intelligence is witnessing a paradigm shift from cloud-based operations to on-device processing. This transformation addresses critical commercial and technical challenges, paving the way for scalable, privacy-focused AI applications across various sectors.

A new white paper released at the end of May by ABI Research attempts to give a global picture of the changes, areas of application, drivers, and constraints for building the case for on-device AI in the consumer and enterprise markets.

Moving AI closer to the user has numerous advantages and marks a significant milestone in the evolution of artificial intelligence. It promises a future where AI is more personal, immediate, and integrated into our daily lives, all while being mindful of privacy and efficiency. By processing data directly on the device, on-device AI users benefit from enhanced privacy, reduced latency, and significant network and cloud services cost savings.

Efficient, localized AI processing is crucial for applications requiring real-time operations, from augmented reality on factory floors to advanced voice and image recognition on smartphones.

Data privacy, cost optimization, and more lifelike human-machine communication will be key value drivers for consumer on-device AI. Examples of consumer on-device AI use cases include health and fitness, where personal data remains local, and the model adapts to the user data to improve insights; more contextualized language translation; all kinds of always-on AI digital assistants with low latency for natural interactions and data privacy through local storage, and even mobile gaming. The possibilities are endless.

Enterprise applications stand to gain significantly from locally processed AI. This includes a wide range of applications such as IoT automated workflows, AI-generated workflow instructions, remote assistance for repair, staff training, machine predictive monitoring in manufacturing; patient monitoring and care, patient history summarization, remote patient assistance and diagnostics, patient chatbots, surgeon training with AI-enabled Extended Reality in healthcare; intelligent route mapping, supply chain tracking in transportation and logistics; dynamic instructions and digital assistant for field technicians in telecom and much more.

For enterprises, adopting on-device AI involves a strategic overhaul to align with emerging technologies. This includes assessing current AI deployments, developing new AI-centric business models, and investing in upskilling programs to cultivate a workforce adept in modern AI technologies.

The concept of hybrid AI, which seamlessly integrates on-device processing with cloud services, represents the next leap in AI’s evolution. This approach promises optimized resource usage across devices and clouds, enhancing application performance without compromising data privacy or operational efficiency.

Despite its benefits, the transition to on-device AI is full of challenges. Power consumption, memory requirements, and managing AI workloads on distributed devices necessitate further chip design and software optimization innovation.

Since its foundation, POLYN has adhered to the on-device inference approach, adding hybrids only when necessary. It is good news that this view on data handling is gaining ground. Of course, we first want to stress the importance of data AI pre-processing on-device, which is just beginning to be recognized across industries. All systems mentioned in the white paper will drown in the data noise without pre-processing.

ABI Research considers generative AI a powerful driver for on-device AI adoption, as it has become very popular lately. Hardware and software innovation has now made it possible to run large generative models, such as Automatic Speech Recognition (ASR), Natural Language Processing (NLP), or image generation workloads, on devices instead of the cloud.

This trend will facilitate deploying smart devices and new AI models into existing processes, enabling users to be more productive and efficient, thanks to potential cost and time savings. Although it is believed that new digital hardware like Neural Processing Units (NPUs) are needed, there are other ways. POLYN is also developing a new concept of NASP 2.0, allowing large neural models to run on small analog chips. NASP 2.0 includes a new patented neural network fragmentation method and an analog programmable memory crossbar. Stay tuned for more about this!