Every 20 years, the world witnesses a leap in computing technology. Data and artificial intelligence (AI) convergence is transforming businesses across industries. As computing power rapidly advances, the hardware needed to support these innovations evolves, too.
Founded in 2015 by Albert Liu, Kneron, a San Diego-based full-stack AI company, has established itself as a global leader in developing edge AI system-on-chip (SoC) processors and solutions for end-edge AI computing.
Albert Liu envisioned Kneron democratizing AI by creating robust, energy-efficient AI solutions that could be integrated into a wide range of devices. His approach offered faster processing and lower costs and addressed global data privacy and security concerns.
Kneron’s Secret: Leading with Expertise
Albert Liu’s professional journey began with a stellar academic foundation at Taiwan’s National Cheng Kung University. His potential led to him receiving a prestigious scholarship from the University of California (UCLA), where he earned his PhD in Electrical Engineering.
Liu’s passion for research was evident from the start, as he actively participated in research programs at UC Berkeley, UCLA, and UC San Diego. Even before founding Kneron in San Diego, he refined his expertise in AI through crucial R&D and management roles at industry leaders such as Qualcomm, Samsung Electronics R&D Center, MStar, and Wireless Information.
These research opportunities allowed Liu to deepen his understanding of AI technology and explore its potential and future possibilities, which is instrumental in shaping Kneron’s mission to remain ahead of AI innovation.
Albert Liu’s contributions to the field are widely recognized. He has over 30 international patents in AI, computer vision, and image processing and more than 70 published papers in leading international journals. His achievements have garnered prestigious awards, including the IEEE Darlington Award and IEEE CTSoc Awards in 2022.
According to Liu, these recognitions highlight his clear vision for Kneron: to develop AI products that are not only technologically advanced but also energy efficient and cost-effective, ensuring their practical application in the real world.
Major Trends in the AI Chipset Market
The AI chipsets market is undergoing significant changes, driven by the rise of specialized AI accelerators designed to optimize the performance of AI algorithms like deep learning and neural networks. These accelerators, including GPUs, TPUs, and FPGAs, are tailored for specific AI tasks, such as image recognition and natural language processing (NLP), enhancing computational efficiency and reducing latency.
Another key trend is the integration of AI at the edge, where AI algorithms are deployed directly on smartphones, Internet of Things (IoT) devices, and autonomous vehicles rather than relying on centralized cloud servers. According to Albert Liu, this approach enables real-time data analysis, reduced latency, and improved privacy and security, meeting the growing demands of AI-driven applications across various industries.
Edge AI & NPUs: Why Now?
As AI applications become more advanced, the demand for specialized processors like neural processing units (NPUs) has grown. NPUs are specifically designed to handle AI tasks in conjunction with other processors more efficiently than traditional GPUs, offering energy-efficient solutions ideal for continuous AI computations such as image generation and face recognition. They play a crucial role in edge AI applications, which address key challenges of cloud AI, including processing speed, cost, and data privacy.
Recognizing the importance of NPUs in the AI landscape, Kneron, under Liu’s leadership, has focused on enhancing these processors to be more efficient, scalable, and business-friendly. As the first company to commercialize NPUs in 2017, Kneron has made notable advancements, optimizing them for real-time, edge AI applications that meet businesses’ evolving needs.
Meeting Industry Demands with Innovation
Kneron’s leadership in AI advancements can be traced back to the visionary design and strategic foresight of its founder, Albert Liu. A key example of Liu’s innovative approach is the Kneron KL830 chip, which boasts an impressively low power consumption of just two watts while delivering powerful AI capabilities. This efficiency is crucial for AI devices to operate continuously without draining energy resources.
In addition, Liu focuses on processing data locally rather than relying on cloud servers or edge AI, leading to solutions like the Foxconn-backed KNEO 330 server. This product supports advanced AI applications, including large language models (LLMs), with accuracy comparable to cloud-based systems.
Albert Liu emphasizes that it reduces costs by 30-40%, provides up to 48 trillion operations per second (TOPs) of AI computing power, and supports up to 8 concurrent connections, making it suitable for small enterprises looking for robust AI solutions.
With these advancements, supported by a world-class research and development team, Liu has secured Kneron over $190 million in funding from prominent investors, including Horizons Ventures, Qualcomm, Sequoia Capital, and Foxconn. It now serves global industry giants like Qualcomm, Sony, Toyota, and Panasonic.
Expanding Kneron’s Impact
As technology advances, Kneron is committed to relentless research and development to stay ahead. Every discovery in AI and related fields represents an opportunity for Albert Liu and Kneron to improve its products and ensure that these are not just adapting but future-ready. The founder is dedicated to refining its NPUs and AI solutions, making them more efficient, scalable, and adaptable to the evolving needs of its global user base.
About Kneron:
Kneron is a full-stack edge AI company founded in San Diego, California, by Albert Liu. The company is known for its edge AI system-on-chip (SoC) processors and solutions for end-edge AI computing. Its AI chips are used for their high-performance computing, advanced algorithm capabilities, and energy efficiency, addressing critical challenges in edge AI devices, such as latency, security, and cost. Its mission is to maximise the potential of AI by providing robust, energy-efficient AI solutions that can be integrated into various devices and complex use-case situations.
Photo courtesy of Kneron
