Where automotive FPGAs stand in smart car designs
By Bob O’Donnell, Lattice Semiconductor
The automotive industry has gotten its fair share of time in the spotlight in recent years. Part of the focus has revolved around how important semiconductors are to modern vehicle designs equipped with smart technology capabilities. These technologies aren’t just limited to the fully electric or hybrid models, which have existed for years now, but have expanded to include SUVs and minivans outfitted with a tablet on the dashboard and a camera built into the rearview mirror. With Deloitte projecting that 45% of the cost of a new car will come from electronic systems by 2030, the importance of chips is only growing.
This is especially true with FPGAs. The most technologically advanced cars can have up to 10-12 FPGAs inside them, all performing varying functions given their inherent flexibility and small size. From infotainment systems, advanced driver assistance systems (ADAS), in-cabin artificial intelligence for human presence detection, and secure battery management, FPGAs are spread up and down vehicle designs and are making cars smarter than ever before.
To read the full article, click here
Related Semiconductor IP
- Process/Voltage/Temperature Sensor with Self-calibration (Supply voltage 1.2V) - TSMC 3nm N3P
- USB 20Gbps Device Controller
- SM4 Cipher Engine
- Ultra-High-Speed Time-Interleaved 7-bit 64GSPS ADC on 3nm
- Fault Tolerant DDR2/DDR3/DDR4 Memory controller
Related White Papers
- How a Standardized Approach Can Accelerate Development of Safety and Security in Automotive Imaging Systems
- Tips and Tricks: Using FPGAs in reliable automotive system design
- Implementing digital processing for automotive radar using SoC FPGAs
- Where Innovation Is Happening in Geolocation. Part 1: Signal Processing
Latest White Papers
- Fault Injection in On-Chip Interconnects: A Comparative Study of Wishbone, AXI-Lite, and AXI
- eFPGA – Hidden Engine of Tomorrow’s High-Frequency Trading Systems
- aTENNuate: Optimized Real-time Speech Enhancement with Deep SSMs on RawAudio
- Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference
- Hardware Acceleration of Kolmogorov-Arnold Network (KAN) in Large-Scale Systems