Members of the Autonomous Computing Consortium include General Motors, Toyota, Denso, Continental, Bosch and NXP.
According to foreign media reports, at the Arm TechCon 2019 conference held in San Jose, California, the British chip technology company Arm, a subsidiary of Japan’s Softbank Group, announced that Arm, as one of the founding members, will cooperate with GM, Toyota and other companies to establish automatic AVCC (Autonomous Vehicle Computing Consortium), which solves various security and computing problems in a collaborative manner.
AVCC members include automotive suppliers such as General Motors, Toyota, DENSO, Continental, Bosch, NXP and Nvidia.
As a mobile chip basic technology company, Arm does not manufacture Chips itself, but through the research of the core technology of microcontroller chips, and then licenses it to major chip manufacturers.
This relationship with the auto industry dates back to the late 1990s. Arm’s general-purpose, real-time processors have been used by major vehicle manufacturers since 1996. Arm’s IP is now widely used in ADAS systems (such as collision avoidance, cruise control, etc.), connectivity, infotainment, powertrain control and other components of the car.
As automakers and tech companies invest in the development of self-driving car technology, some industry analysts expect the market’s demand for automotive chips to grow rapidly.
But current test tools used to develop self-driving software use large, power-hungry chips in data centers. And the auto industry, chip companies and automakers are increasingly agreeing that in order to meet autonomous driving needs, the power and size of test tools must be slashed to one-tenth or less of current systems.
Dipti Vachani, senior vice president of Arm’s automotive and IoT business line, also said that the realization of the dream of autonomous vehicles seems to be within people’s reach, bringing safer roads, easier transportation and more sustainable cities. potential.
But Dipti Vachani believes that there are still significant challenges in the deployment of vehicles: including ultra-high performance computing within the power, thermal and size constraints of the vehicle to run large and complex autonomous software stacks.
Therefore, after understanding the technical complexities and obstacles that need to be overcome to deploy autonomous vehicles, the first task of the AVCC computing platform is to build a general computing system for autonomous driving. The aim is to make it easier for car companies to write software that works on chips from different manufacturers, just as software based on Microsoft Windows can work on processors from Intel or AMD.
But delivering self-driving cars is not a one-size-fits-all effort—it requires collaboration at the industry level. Dipti Vachani said that to ensure that technologies from different suppliers work well together, the AVCC consortium will collaborate on issues such as safety, computing and software for autonomous vehicles.
The disruptor of autonomous driving chips
In fact, this is not the first time Arm has tested the waters in the field of autonomous driving. Last September, Arm launched its “Safety Ready” program, which aims to provide solutions for autonomous vehicles. At present, Arm is providing solutions for L3-level autonomous driving, and will provide related products for L4-level and L5-level autonomous driving around 2020.
In addition, Arm also launched a product code-named Cortex-A76AE, the first processor built specifically for autonomous vehicles. The processor allows chipmakers to design chips with safety features that enable self-driving cars to meet the most stringent safety requirements, enabling features such as autonomous avoidance to be applied to the car.
The new product line is named AE, which stands for “Automotive Enhanced”.
In December last year, Arm launched a new processor product, the Cortex-A65AE, which is suitable for autonomous driving. According to the company’s expectations, the first cars using the Cortex-A76AE processor will hit the road in 2020, and the Cortex-A65AE will also be available in 2020.
Lakshmi Mandyam, vice president of Arm’s automotive business, said in an interview earlier: “Currently, cars with autonomous driving capabilities are forced to install data processing equipment in the trunk of the car and consume a lot of energy, but we believe that from the perspective of energy saving, It seems that Arm’s processors should be able to reduce energy consumption by more than 10 times.”
This is seen as a preview of Arm’s entry into the field of autonomous driving and competition with rivals Mobileye and Nvidia.
In contrast, the chip giants Mobileye and Nvidia, which have entered the game, are obviously much faster in their layout and expansion in the field of autonomous driving.
Mobileye has gradually moved from the “black box” model of the all-in-one vision chip + algorithm supplier in the past to the open eyeQ5 chip (that is, allowing third-party code to run).
According to Mobileye’s plan, by mid-2020, it will provide partners with a complete set of autonomous vehicle subsystems, such as its surround computer vision suite – 360 degrees, 12 cameras, 300-yard range vision system and multi-chip communication key solutions etc.
In the past year, Mobileye has secured 20 new project orders covering 78 models from 16 OEMs and 5 Tier 1 suppliers.
Nvidia also launched a complete L2+ ADAS system this spring, called DRIVE AutoPilot, for component suppliers and OEMs. Its AutoPilot relies on NVIDIA’s DRIVE AGX Xavier SoC and its driver software platform.
Nvidia predicts that by 2020, the first car with DRIVE AutoPilot will be in production.
Therefore, from the time point of production and listing, although Arm entered the game late, it is not necessarily much behind Mobileye and Nvidia.
The establishment of the Autonomous Driving Computing Alliance shows that Arm is also closely following the layout of the automotive industry chain. With the addition of two automakers (GM, Toyota), three industry suppliers (Bosch, Denso, Continental), and two semiconductor companies (NVIDIA, NXP), Arm’s security and computing challenges are expected to be in collaboration If it is solved, its layout in the autonomous vehicle industry will also be further opened up.
Members of the Autonomous Computing Consortium include General Motors, Toyota, Denso, Continental, Bosch and NXP.
According to foreign media reports, at the Arm TechCon 2019 conference held in San Jose, California, the British chip technology company Arm, a subsidiary of Japan’s Softbank Group, announced that Arm, as one of the founding members, will cooperate with GM, Toyota and other companies to establish automatic AVCC (Autonomous Vehicle Computing Consortium), which solves various security and computing problems in a collaborative manner.
AVCC members include automotive suppliers such as General Motors, Toyota, DENSO, Continental, Bosch, NXP and Nvidia.
As a mobile chip basic technology company, Arm does not manufacture chips itself, but through the research of the core technology of microcontroller chips, and then licenses it to major chip manufacturers.
This relationship with the auto industry dates back to the late 1990s. Arm’s general-purpose, real-time processors have been used by major vehicle manufacturers since 1996. Arm’s IP is now widely used in ADAS systems (such as collision avoidance, cruise control, etc.), connectivity, infotainment, powertrain control and other components of the car.
As automakers and tech companies invest in the development of self-driving car technology, some industry analysts expect the market’s demand for automotive chips to grow rapidly.
But current test tools used to develop self-driving software use large, power-hungry chips in data centers. And the auto industry, chip companies and automakers are increasingly agreeing that in order to meet autonomous driving needs, the power and size of test tools must be slashed to one-tenth or less of current systems.
Dipti Vachani, senior vice president of Arm’s automotive and IoT business line, also said that the realization of the dream of autonomous vehicles seems to be within people’s reach, bringing safer roads, easier transportation and more sustainable cities. potential.
But Dipti Vachani believes that there are still significant challenges in the deployment of vehicles: including ultra-high performance computing within the power, thermal and size constraints of the vehicle to run large and complex autonomous software stacks.
Therefore, after understanding the technical complexities and obstacles that need to be overcome to deploy autonomous vehicles, the first task of the AVCC computing platform is to build a general computing system for autonomous driving. The aim is to make it easier for car companies to write software that works on chips from different manufacturers, just as software based on Microsoft Windows can work on processors from Intel or AMD.
But delivering self-driving cars is not a one-size-fits-all effort—it requires collaboration at the industry level. Dipti Vachani said that to ensure that technologies from different suppliers work well together, the AVCC consortium will collaborate on issues such as safety, computing and software for autonomous vehicles.
The disruptor of autonomous driving chips
In fact, this is not the first time Arm has tested the waters in the field of autonomous driving. Last September, Arm launched its “Safety Ready” program, which aims to provide solutions for autonomous vehicles. At present, Arm is providing solutions for L3-level autonomous driving, and will provide related products for L4-level and L5-level autonomous driving around 2020.
In addition, Arm also launched a product code-named Cortex-A76AE, the first processor built specifically for autonomous vehicles. The processor allows chipmakers to design chips with safety features that enable self-driving cars to meet the most stringent safety requirements, enabling features such as autonomous avoidance to be applied to the car.
The new product line is named AE, which stands for “Automotive Enhanced”.
In December last year, Arm launched a new processor product, the Cortex-A65AE, which is suitable for autonomous driving. According to the company’s expectations, the first cars using the Cortex-A76AE processor will hit the road in 2020, and the Cortex-A65AE will also be available in 2020.
Lakshmi Mandyam, vice president of Arm’s automotive business, said in an interview earlier: “Currently, cars with autonomous driving capabilities are forced to install data processing equipment in the trunk of the car and consume a lot of energy, but we believe that from the perspective of energy saving, It seems that Arm’s processors should be able to reduce energy consumption by more than 10 times.”
This is seen as a preview of Arm’s entry into the field of autonomous driving and competition with rivals Mobileye and Nvidia.
In contrast, the chip giants Mobileye and Nvidia, which have entered the game, are obviously much faster in their layout and expansion in the field of autonomous driving.
Mobileye has gradually moved from the “black box” model of the all-in-one vision chip + algorithm supplier in the past to the open eyeQ5 chip (that is, allowing third-party code to run).
According to Mobileye’s plan, by mid-2020, it will provide partners with a complete set of autonomous vehicle subsystems, such as its surround computer vision suite – 360 degrees, 12 cameras, 300-yard range vision system and multi-chip communication key solutions etc.
In the past year, Mobileye has secured 20 new project orders covering 78 models from 16 OEMs and 5 Tier 1 suppliers.
Nvidia also launched a complete L2+ ADAS system this spring, called DRIVE AutoPilot, for component suppliers and OEMs. Its AutoPilot relies on NVIDIA’s DRIVE AGX Xavier SoC and its driver software platform.
Nvidia predicts that by 2020, the first car with DRIVE AutoPilot will be in production.
Therefore, from the time point of production and listing, although Arm entered the game late, it is not necessarily much behind Mobileye and Nvidia.
The establishment of the Autonomous Driving Computing Alliance shows that Arm is also closely following the layout of the automotive industry chain. With the addition of two automakers (GM, Toyota), three industry suppliers (Bosch, Denso, Continental), and two semiconductor companies (NVIDIA, NXP), Arm’s security and computing challenges are expected to be in collaboration If it is solved, its layout in the autonomous vehicle industry will also be further opened up.
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