New Architectures for Automotive Intelligence

My first car was a used 1971 Volvo 142 and probably did not contain more than a handful of transistors. I used to joke that it could easily survive the EMP from a nuclear explosion. Now, of course, cars contain dozens or more processors, DSP’s and other chips containing millions of transistors. It’s widely expected that the number of CPU’s alone could run into the hundreds as new infotainment and autonomous driving features are added.

Automotive intelligence electronics are rapidly evolving, but relatively speaking are in their infancy. The best arguments for this assertion are the huge changes forecast for powertrain, Infotainment, automation, safety and connectivity in cars for the foreseeable future. With rapid change and its relative youth, we can expect dramatic evolution of the internal architecture of automotive electronics. This evolution will recapitulate the evolution of computing and the internet. After all cars are a microcosm of the larger computing landscape.

We see each player in the market looking to shape the prevailing architecture around their own product strengths. Qualcomm, Nvidia, NXP, Cadence and Synopsys and many others, each have their own computing paradigm. Nvidia of course if pushing for centralized GPU based processing, Qualcomm is looking to leverage 5G and communication. Vision processing IP providers are proselytizing for their products.

The growth of the internet led to the expansion of distributed computing, and consequently computation work moved from mainframes to local nodes. Eventually IoT combined the models with edge sensor fusion and central processing. It’s likely that in cars sensor fusion will take place closer to the sensors, and central processing will be used for tasks that require integrated data from multiple automotive systems.

I had a chance recently to read a white paper by Achronix that explores the choices and coming evolution of onboard computing. Achronix posits that immense amounts of data will be generated by onboard sensors, which in turn will place heavy demands on data links processing units and strain power distribution and dissipation abilities. Also, they mention that reliability as enabled by real-time testing and diagnostics will become even more important. Achronix offers a unique option to ameliorate reliability, power, data and processing issues. Their embedded FPGA fabric, known as Speedcore eFPGA can work in multiple ways to improve and futureproof automotive systems.

As systems move toward sensor fusion at the edge, having SOC’s with processors and programmable eFPGA fabric will improve throughput and allow for flexibility as the needs for processing algorithms change. CPU’s will not have to intermediate all data transfers because eFPGA fabric can perform DMA without requiring CPU IRQ’s. The ability to perform lookaside processing will be a major factor in system performance.

SOC’s with embedded FPGA fabric can help manage the onboard data networks – including Ethernet, as well as legacy and future automotive networks. These SOC’s will be optimized for packet handling and data filtering on the fly.

Finally, higher level processing can also benefit by hardware acceleration through eFPGA. FPGA’s are already being used for this in data centers, but eFPGA avoid costly SerDes transfers, higher part counts, and overprovisioned general purpose commercial parts.

However, eFPGA comes into its own when we talk about reliability. Each eFPGA core can become a real-time embedded hardware diagnostic engine if needed. With full bus access and reprogrammability, eFPGA can be used to generate tests to ascertain the operating condition of chips and system in running vehicles or during servicing.

The Achronix white paper, entitled Speedcore eFPGA in Automotive Intelligence Applications does a good job of introducing the issues faced by automotive system designers. It also covers several approaches to Automotive Intelligence and closes by outlining the ways that eFPGA can improve overall system performance.