Optimized AI Models to Turbocharge the Future of Smart Devices

Through digitalization and transformational technology, companies and organizations in various industries and with different applications can facilitate collaborative software and network. Another digitalization benefit is delivering a Return on Investment (ROI)-oriented marketing approach. If a company's investment strategies are generated by a consideration of business metrics like growth and revenues, the future processes can be automated accordingly.

Since the COVID-19 pandemic, the number of global companies seeking modern solutions to grow their revenues increases as digitalization goes on. The Internet of Things (IoT) has become such a solution, delivering a vast range of offerings for the most challenging cases, and providing connectivity in multiple sectors. With IoT, intelligent and automated venues can decrease costs and increase business revenues. However, widespread digitalization requires ubiquitous connectivity and scalable computing resources. As such, hyperscalers like Google have arranged their path to private 5G and other cloud computing interests. They help companies that meet various obstacles on their digitalization path to overcome and transform their management networks.

Nonetheless, automation is not an easy process. A fully digital enterprise asset or workflow requires a fully optimized technology stack, ranging from connectivity to compute, device management, security, application enablement, data analytics, and Artificial Intelligence (AI). Companies often need to rely on internal developers and IoT technology suppliers to integrate and optimize the solutions for their custom requirements, particularly in AI.

Qualcomm, the market’s leading wireless technology developer, is concentrated on the 5G expansion and is set to form unique technologies that will be successful in smart devices. In December 2021, Qualcomm launched its seventh generation Artificial Intelligence (AI) Engine and became the first Google Cloud System on a Chip (SoC) customer on Neural Architecture Search (NAS). This partnership provides developers with automatically optimized AI models for IoT, mobile, Extended Reality (XR), and automotive platforms. On the other side, Vertex AI, Google’s managed Machine Learning (ML) platform, offers to take advantage of suitable ML tools within one AI platform. Low-power devices, such as IoT, through Vertex AI NAS, receive high accuracy and low latency AI, providing memory and energy efficiency and delivering optimized AI models to Qualcomm Technologies’ customers in weeks instead of months. In addition, the AI models are optimized for their intended environment, have a smaller footprint with around 80% less code than other competitive platforms, and require less training time.

More importantly, bringing AI to diverse device types and application areas, such as those applying IoT, is no longer a question of if, but when. Developers can utilize Qualcomm Neural Processing Software Development Kit (SDK) to develop neural network for Qualcomm Technologies’ IoT platforms. The Qualcomm Neural Processing SDK also ensures constant optimizations and performance of Snapdragon-powered devices. Equipped with Qualcomm AI Engine, Qualcomm Snapdragon SoC is based on the 5G connection and developed with an upgradeable architecture to facilitate the immediate rollout of recent features across settled industrial IoT, wireless connections, and 5G private networks through software advancements.

Current AI partnerships manage the intelligent aspects of the linked smart edge. These crucial engineering dots in the complex of innovations create the foundation of the future infrastructure layer that determines vastly transformational technological progress. The possible speed and widely spread hardware connections lead to the increasing intelligent edge connections tendency several times during the future decades. Through AI in IoT, mobile, XR, and automotive platforms, enterprises are increasingly automating key tasks such as human and asset tracking, high precision object, text, and sound recognition, material handling, autonomous navigation, condition-based monitoring, surveillance, and security. This is a representation of the ecosystem where a huge potential for the tech segment meets the future digital transformation.

However, defining this ecosystem as part of a more complicated and interconnected whole means its significance rapidly evolves considerably more, made obvious by the involved operational strategies. Moreover, given how AI has already become influential to the varieties of segments for which chipsets must now produce at the market to supply intelligent edge devices and systems, the demand and importance for them would only increase. Total global shipments of IoT devices are expected to grow to 23.7 billion by 2026, while ABI Research estimates the total global shipments of very edge AI-enabled devices will reach 15.2 million in 2020. Armed with the advanced NAS solution and ML development platform from Google, Qualcomm is preparing a solid foundation for AI developers to develop innovative applications across IoT, mobile, XR, and automotive platforms.