Sensor fusion has been discussed for years for a diverse array of applications. However, it acquires a highly specialized design premise when it comes to automotive applications like advanced driver assistance systems (ADAS) and autonomous vehicles (AVs). Perception and sensor fusion systems are among the highly complex areas in ADAS and AV designs from a computational standpoint as they crunch all the data and determine what a vehicle is seeing. More specifically, sensor fusion provides the ability to merge information from radars, lidar (light detection and ranging) and cameras to produce a single model of the space around a vehicle--a crucial capability for ADAS and AV designs. This model is created as a result of balancing the strengths of the various sensors to formulate a more accurate picture of vehicle surroundings.

As 2022 comes to an end, EE Times is highlighting memorable stories from each of its editors over the past year. Today's spotlight is on Sally Ward-Foxton, a correspondent at EE Times. Sally covers AI topics for EE Times and the EE Times Europe magazine. She has spent the last 18 years writing about the electronics industry from London, U.K., and has written for Electronic Design, ECN, Electronic Specifier: Design, Components in Electronics, and many more. She holds a master's degree in Electrical and Electronic Engineering from the University of Cambridge.

ALISO VIEJO, Calif., August 26, 2021--(BUSINESS WIRE)--BrainChip Holdings Ltd (ASX: BRN), (OTCQX: BRCHF), a leading provider of ultra-low power high performance artificial intelligence technology, was recognized as one of the "Startups Worth Watching in 2021" in EE Times' annual Silicon 100 list of global semiconductor technologies. EE Times' 21st revision of the Silicon 100 tracks the pulse of the industry to identify emerging technology trends and developments that hold promise for the future. This year, the publication chose to analyze the Silicon 100 in more detail with 22 categories that run from materials and packaging at a fundamental extreme to quantum computing and security at the highest level of abstraction. BrainChip was recognized in the "Specialist (GPU-Through-AI) Processor, Accelerators" category. Selection of companies to the Silicon 100 is based on criteria including technology, intended market, financial position and investment profile, maturity and executive leadership.

A staggering amount of money is pouring into data center AI chip companies at the moment. Data center AI chip companies are raising eye-watering amounts of money. In the last week, we've seen Groq announce a $300 million Series C round of funding, and SambaNova raise a staggering $676 million Series D. SambaNova is now valued at somewhere above $5 billion. They are not the only ones in this sector raising these huge amounts of money. Fellow data center AI chip companies Graphcore (raised $710 million, valued at $2.77 billion) and Cerebras (raised more than $475 million, valued at $2.4 billion) are hot on their heels as the sector continues to gain momentum.

LeapMind (Tokyo, Japan) announced its entry into the processor IP business with Efficiera, an ultra-low-power AI inference accelerator IP product. Efficiera is optimized for models that have been heavily quantized using LeapMind's'extremely low-bit quantization' software techniques. It is designed for convolutional neural networks (CNNs), the type of network typically used for image processing and analysis tasks today. "This is the company's first hardware IP product. But we are working on the development of a core technology called extreme quantization technology that operates at both software and hardware-IP levels with a network optimized for practical applications and a dedicated compiler," a LeapMind spokesperson told EE Times.

Robocars will not be accident-free. For regulators who harbor hopes of fostering a future of autonomous vehicles (AVs), this is a political reality that's likely to haunt them. For the public, it's a psychologically untenable prospect, especially if a robocar happens to flatten a loved one. From a technological standpoint, though, this inevitability is the starting point for engineers who want to develop safer AVs. "The safest human driver in the world is the one who never drives," said Jack Weast, Intel's senior principal engineer and Mobileye's vice president for autonomous vehicle standards.

Intel is pushing for Responsibility-Sensitive Safety (RSS), a mathematical model for autonomous-vehicle safety conceived by Mobileye (now an Intel company), to become an IEEE standard. The company is spearheading a new working group, IEEE P2846, to pursue "A Formal Model for Safety Considerations in Automated Vehicle Decision Making." The group's first meeting is scheduled for late January in San Jose, Calif. More specifically, the working group seeks to enable industry and government to "align on a common definition of what it means for an automated vehicle to drive safely balancing safety and practicability." Intel sees the initiative as a way to encourage autonomous-vehicle industry to ask -- and grapple with answering -- the hardest question of all in the AV era: How safe is safe enough?

Voice, connectivity and AI took center stage at the Consumer Electronics Show last week. If this year's CES is any indication, these three building blocks will compose the holy trinity of consumer electronics devices that will drive the market in 2018 and further into the future. Voice assistants are now poised to move into wearables, headphones, baby monitors, lamps, TV remotes and vehicles. Paul Beckmann, founder and chief technology officer of DSP Concepts, told EE Times, "We are witnessing a Cambrian explosion around voice." At CES, Baidu, known as "China's Google," shouted out most loudly for voice by unveiling and opening to developers its Duer OS-based platform.

IBM Zurich researchers have developed a generic artificial-intelligence preprocessing building block for accelerating Big Data machine learning algorithms by at least 10 times over existing methods. The approach, which IBM presented Monday (Dec. "Our motivation was how to use hardware accelerators, such as GPUs [graphic processing units] and FPGAs [field-programmable gate arrays], when they do not have enough memory to hold all the data points" for Big Data machine learning, IBM Zurich collaborator Celestine Dünner, co-inventor of the algorithm, told EE Times in advance of the announcement. "To the best of our knowledge, we are first to have generic solution with a 10x speedup," said co-inventor Thomas Parnell, an IBM Zurich mathematician. "Specifically, for traditional, linear machine learning models -- which are widely used for data sets that are too big for neural networks to train on -- we have implemented the techniques on the best reference schemes and demonstrated a minimum of a 10x speedup."

Intel's $1 billion investment in the AI ecosystem is one of the well-publicized talking points at the processor company. The Intel empire boasts a breadth of AI technologies it has amassed by acquisition and Intel Capital investments in AI startups. The acquired companies seemingly useful to Intel's AI ambitions thus far include Altera (2015), Saffron (2015), Nervana (2016), Movidius (2016) and Mobileye (2017). Intel Capital has also fattened its AI portfolio with startups Mighty AI, Data Robot, Lumiata, CognitiveScale, Aeye Inc., Element AI and others. Unclear is how Intel is going to stitch all this together.