The network of power supplies, sensors, and electronics that is used for Advanced Driver Assistance Systems (ADAS) – features of which include emergency braking, adaptive cruise control, and self-parking systems – is extensive, with the effectiveness of ADAS being determined by the accuracy of the sensing equipment coupled with the accuracy and speed of analysis of the on-board autonomous controller.

The on-board analysis is where artificial intelligence comes into play and is a crucial element to the proper functioning of autonomous vehicles.

In market research company IDTechEx’s recent report on AI hardware at the edge of the network, “AI Chips for Edge Applications 2024 – 2034: Artificial Intelligence at the Edge”, AI chips (those pieces of semiconductor circuitry that are capable of efficiently handling machine learning workloads) are projected to generate revenue of more than USD$22 billion by 2034, and the industry vertical that is to see the highest level of growth over the next ten year period is the automotive industry, with a compound annual growth rate (CAGR) of 13%.

The part that AI plays

The AI chips used by automotive vehicles are found in centrally located microcontrollers (MCUs), which are, in turn, connected to peripherals such as sensors and antennae to form a functioning ADAS.

On-board AI compute can be used for several purposes, such as driver monitoring (where controls are adjusted for specific drivers, head and body positions are monitored in an attempt to detect drowsiness, and the seating position is changed in the event of an accident), driver assistance (where AI is responsible for object detection and appropriate corrections to steering and braking), and in-vehicle entertainment (where on-board virtual assistants act in much the same way as on smartphones or in smart appliances).

The most important of the avenues listed above is the latter, driver assistance, as the robustness and effectiveness of the AI system determines the vehicle’s autonomous driving level.

Since its launch in 2014, the SAE Levels of Driving Automation (shown below) have been the most-cited source for driving automation in the automotive industry, which defines the six levels of driving automation.

These range from level 0 (no driving automation) to level 5 (full driving automation). The current highest state of autonomy in the private automotive industry (incorporating vehicles for private use, such as passenger cars) is SAE Level 2, with the jump between level 2 and level 3 being significant, given the relative advancement of technology required to achieve situational automation.

A scalable roadmap

SoCs for vehicular autonomy have only been around for a relatively short amount of time, yet it is clear that there is a trend towards smaller node processes, which aid in delivering higher performance.

This makes sense logically, as higher levels of autonomy will necessarily require a greater degree of computation (as the human computational input is effectively outsourced to semiconductor circuitry).

The above graph collates the data of 11 automotive SoCs, one of which was released in 2019, while others are scheduled for automotive manufacturers’ 2024 and 2025 production lines.

Among the most powerful of the SoCs considered are the Nvidia Orin DRIVE Thor, which is expected in 2025, where Nvidia is asserting a performance of 2000 Trillion Operations Per Second (TOPS), and the Qualcomm Snapdragon Ride Flex, which has a performance of 700 TOPS and is expected in 2024.

Moving to smaller node sizes requires more expensive semiconductor manufacturing equipment (particularly at the leading edge, as Deep Ultraviolet and Extreme Ultraviolet lithography machines are used) and more time-consuming manufacture processes.

As such, the capital required for foundries to move to more advanced node processes proves a significant barrier to entry to all but a few semiconductor manufacturers.

This is a reason that several IDMs are now outsourcing high-performance chip manufacture to those foundries already capable of such fabrication.

In order to keep costs down for the future, it is also important for chip designers to consider the scalability of their systems, as the stepwise movement of increasing autonomous driving level adoption means that designers that do not consider scalability at this juncture run the risk of spending more for designs at ever-increasing nodes.

Given that 4 nm and 3 nm chip design (at least for the AI accelerator portion of the SoC) likely offers sufficient performance headroom up to SAE Level 5, it behooves designers to consider hardware that is able to adapt to handling increasingly advanced AI algorithms.

It will be some years until we see cars on the road capable of the most advanced automation levels proposed above, but the technology to get there is already gaining traction. The next couple of years, especially, will be important ones for the automotive industry.

Report coverage

IDTechEx forecasts that the global AI chips market for edge devices will grow to US$22.0 billion by 2034, with AI chips for automotive accounting for more than 10% of this figure.

IDTechEx’s report gives analysis pertaining to the key drivers for revenue growth in edge AI chips over the forecast period, with deployment within the key industry verticals – consumer electronics, industrial automation, and automotive – reviewed.

Case studies of automotive players’ leading system-on-chips (SoCs) for ADAS are given, as are key trends relating to performance and power consumption for automotive controllers.

More generally, the report covers the global AI Chips market across eight industry verticals, with 10-year granular forecasts in six different categories (such as by geography, by chip architecture, and by application). IDTechEx’s report “AI Chips for Edge Applications 2024 – 2034: Artificial Intelligence at the Edge” answers the major questions, challenges, and opportunities the edge AI chip value chain faces.

[For further understanding of the markets, players, technologies, opportunities, and challenges, please refer to the report].

SOCs from these – and other industries – are likely to face more reoccurring targeted attacks, as will supply chain attacks via telecommunication providers.

Another threat awaiting SOCs is more initial compromises through public-facing applications. Organisations that are threatened by ransomware attacks might also encounter data destruction. From an internal point of view, SOC teams face personnel shortages and increasing demand for efficiencies.

As the role of cybersecurity in large businesses increases remarkably year-on-year, SOCs are of paramount importance as effectively organised teams can secure their business from rapidly evolving malware and attacks methods.

This year’s Kaspersky Security Bulletin ends with tailored predictions for SOCs.

More reoccurring targeted attacks by state-sponsored actors  

In 2022, Kaspersky’s experts saw the average number of incidents in the mass media sector double, growing from 263 in 2021, to 561 in 2022.

During the last year a number of high-profile cases occurred, including when Iranian state TV broadcasting was interrupted by hackers during protests in the country. Media outlets were also subject to DDoS attacks, for example, such as those in Czech Republic.

Alongside the government sector, where the average number of incidents increased by 36% in 2022, mass media became the prime target for cybercriminals among the 13 other analysed segments including industrial, food, development, financial, and others.

The growth will continue in 2023, with reoccurring targeted attacks by state-sponsored actors likely to be often observed. 

While this is normally relevant for government organisations, the mass media segment has been increasingly targeted during international conflicts that are traditionally accompanied by information warfare where mass media inevitably play an important role.

“Large businesses and government agencies have always been targets of cybercriminals and state-sponsored actors, but geopolitical turbulence increased attackers’ motivations and enlivened hacktivism, which cybersecurity specialists have not regularly encountered until 2022. The new wave of politically-motivated attacks is especially relevant for the government and mass media sectors. To effectively protect a company, it’s necessary to implement a comprehensive threat detection and remediation provided through Managed Detection and Response services,” said Sergey Soldatov, Head of Security Operation Center (SOC) at Kaspersky.

Supply chain attacks via telecommunication providers

In 2023, perpetrators may strike supply chains by attacking telecommunication companies more. This is a further attempt to hit customers, so the growing threat looms larger this year.

In 2021, the telecom industry saw – for the first time – a prevalence of high severity incidents throughout the year. Although in 2022, the average share of high severity incidents was lower – 79 in 2021 per 10k systems monitored, versus roughly 12 in 2022 – these companies remain attractive targets for cybercriminals.

Ransomware destroyers; initial compromises via public-facing applications

Throughout 2022, Kasperksy observed a new ransomware trend that will continue in 2023 – ransomware actors will not only encrypt companies’ data but also destroy it. This is relevant for organisations which are subject to politically-driven attacks.

Another threat awaiting SOCs is more initial compromises through public-facing applications. Penetration from the perimeter requires less preparation than phishing and old vulnerabilities are still exposed.

What SOCs will face internally? Processes and efficiency

In 2023, the value every team member (even not highly skilled ones) brings to SOC is increasing. Developing the skills of the team is the proven way to counter the increasing number of threats. That means IR-related training and any form of SOC exercises, such as TTX, purple teaming, and advisory attack emulations, will be of vital importance.

The growing threat landscape leads to increasing budgets and demand for more efficiencies. Increasing numbers of incidents and threats transforms into a need to predict attacks and techniques, raising the value of threat intelligence and hunting.