Nvidia is planning to release three new chips for China, according to local media reports, weeks after the US blocked it from selling two high-end artificial intelligence (AI) chips and one of its top gaming chips to Chinese firms. Nvidia declined to comment when asked about the report. Last month, the US AI chip giant said new export restrictions announced by Washington would block it from selling two of its modified advanced AI chips - the A800 and H800 - both of which were created for the Chinese market last year to comply with previous export rules. One of the company's top-of-the-line gaming chips, the L40S chip, which it announced in August, would also be affected, it said. On 24 October, Nvidia said those curbs would take immediate effect, as US regulators had sped up an original deadline.

The Google team has developed a new AI model that can design complex chips in just hours. This is an incredibly difficult task and usually takes months for human engineers to accomplish. Let's look into what this new artificial intelligence microchip is and the potential impact it could make in the technology industry. A microchip is a small electronic device that controls and stores electronic data. It is made up of a silicon chip that has been fabricated into a very small size.

IBM has announced its new chip, Telum – a new CPU chip that will allow IBM clients to leverage deep learning inference at scale. The new chip features a centralised design, which allows clients to leverage the full power of the AI processor for AI-specific workloads, making it ideal for financial services workloads like fraud detection, loan processing, clearing and settlement of trades, anti-money laundering, and risk analysis. A Telum-based system is planned for the first half of 2022. "Our goal is to continue improving AI hardware compute efficiency by 2.5 times every year for a decade, achieving 1,000 times better performance by 2029," said IBM in a press release. The chip contains eight processor cores, running with more than 5GHz clock frequency, optimised for the demands of enterprise-class workloads.

Fresh off a $40 million Series A round, edge AI specialist Kneron today announced the launch of its newest custom chip, the Kneron KL 720 SoC. With funding from the likes of Alibaba, Sequoia, Horizons Ventures, Qualcomm and SparkLabs Taipei (as well as a few undisclosed backers), it's worth taking the company's efforts seriously, and Kneron has no qualms about comparing its chips to those of Intel and Google, for example. It argues that its KL 720 is twice as energy efficient as Intel's latest Movidius chips and four times more efficient than Google's Coral Edge TPU at running the MobileNetV2 image recognition benchmark. Compared to its previous generation of chips, this updated version can process 4K still images and videos at a 1080P resolution. It also features a number of new audio recognition breakthroughs for the company, which Kneron says will allow devices that use its chips to bypass the standard wake words on other chips and have immediate conversations with the device.

The news: A new type of artificial eye, made by combining light-sensing electronics with a neural network on a single tiny chip, can make sense of what it's seeing in just a few nanoseconds, far faster than existing image sensors. Why it matters: Computer vision is integral to many applications of AI--from driverless cars to industrial robots to smart sensors that act as our eyes in remote locations--and machines have become very good at responding to what they see. But most image recognition needs a lot of computing power to work. Part of the problem is a bottleneck at the heart of traditional sensors, which capture a huge amount of visual data, regardless of whether or not it is useful for classifying an image. Crunching all that data slows things down.

The chip designer Ambarella has announced two new chips for automotive cameras and advanced driver assistance systems (ADAS) based on its CVflow architecture for artificial intelligence processing. The Santa Clara, California-based company introduced the CV22FS and CV2FS automotive camera (SoC) systems with CVflow AI processing and ASIL-B compliance to enable critical safety applications. Ambarella will also demonstrate applications with its existing chips, as well as a robotic platform and Amazon SageMaker Neo technology to train machine learning models, at CES 2020, the big technology fair in Las Vegas this week. The company, which was made public in 2011, started as a manufacturer of low-power chips for video cameras. But he turned that ability into computer vision experience and launched his CVflow architecture in 2018 to create low-power artificial intelligence chips.

The Hanguang 800 is being implemented across many application scenarios within Aliyun, ranging from video classification to smart city applications. For example, the company's popular Pailitao platform applies visual image search to e-commerce, allowing customers to search for items by taking a photo of the query object. Using AI-based image recognition & indexing powered by the new Hanguang 800, Aliyun can increase image processing efficiency by 12 times compared to GPUs. With regard to smart city tech, Aliyun says it previously used 40 traditional GPUs to process videos of central Hangzhou with a latency of 300ms. Now the task requires only four Hanguang 800 with a lower latency of 150ms.

Roundup Here's this week's collection of AI-related news that we found interesting. Read on to find out more about a new chip coming to Microsoft Azure and how Twitter hopes to deal with deepfakes. Graphcore ML chips coming to Microsoft Azure: Graphcore, a British AI hardware startup, is teaming up with Microsoft to bring its Intelligence Processing Unit chip to cloud users. "The Graphcore IPU is unique in keeping the entire machine learning knowledge model inside the processor," it said this week. "With 16 IPU processors, all connected with IPU-Link technology in a server, an IPU system will have over 100,000 completely independent programs, all working in parallel on the machine intelligence knowledge model."

Roundup Here's this week's collection of AI-related news that we found interesting. Read on to find out more about a new chip coming to Microsoft Azure and how Twitter hopes to deal with deepfakes. Graphcore ML chips coming to Microsoft Azure: Graphcore, a British AI hardware startup, is teaming up with Microsoft to bring its Intelligence Processing Unit chip to cloud users. "The Graphcore IPU is unique in keeping the entire machine learning knowledge model inside the processor," it said this week. "With 16 IPU processors, all connected with IPU-Link technology in a server, an IPU system will have over 100,000 completely independent programs, all working in parallel on the machine intelligence knowledge model."

Alibaba is well aware of the growing demand for dedicated compute to power today's AI applications. Last year, the Hangzhou-based tech giant launched its semiconductor subsidiary Pingtouge ("Honey Badger" in Chinese) to develop embedded chip and neural network accelerators. At the time, Alibaba CTO Jeff Zhang pledged Pingtouge would produce the world's most advanced neural network chip by the middle of this year. Today, Alibaba kept its promise. At the Alibaba Cloud (Aliyun) Apsara Conference 2019, Pingtouge unveiled its first AI dedicated processor for cloud-based large-scale AI inferencing.