Tesla’s shift from FPGA/GPU-based systems to custom ASICs (e.g., FSD Chip, Dojo D1) represents a strategic divergence from industry norms. Here’s how FPGA solutions compare to Tesla’s approach in LiDAR/vision processing:

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1. Architectural Comparison

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2. Sensor Processing Capabilities
FPGAs (e.g., Xilinx for LiDAR)

Strengths:

  • Handle multi-sensor fusion (LiDAR + radar + cameras) with parallel pipelines.
  • Support legacy protocols (CAN FD, Automotive Ethernet).

Weaknesses:

  • Higher power than ASICs for equivalent TOPS.
  • Limited AI acceleration (vs. dedicated NPUs).

Tesla’s ASICs (Vision-Only)

Strengths:

  • Custom SRAM-on-chip (32MB cache for FSD Chip) reduces DRAM latency.
  • Dojo’s 1.1 EFLOPS supercomputer trains vision models at scale.

Weaknesses:

  • No native LiDAR/radar support (relies purely on cameras).
  • Inflexible to new sensor modalities (e.g., 4D radar).

3. Real-World Implementations
FPGA Examples:

  • Luminar Iris (Xilinx FPGA): Processes 200m+ LiDAR data for Volvo.
  • Continental ARS540 (Xilinx RFSoC): 4D imaging radar with MIMO processing.

Tesla’s ASIC Examples:

FSD Chip (HW3/HW4):

  • 144 TOPS (HW3) → 256 TOPS (HW4) for 8-camera vision pipelines.
  • Uses dual NPUs for transformer-based “HydraNet” models.

Dojo D1 Tile:

  • 362 TFLOPS (FP32) for autonomous training.
  • 1.25TB/s bandwidth via custom interconnect.

4. Cost & Scalability

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5. Future-Proofing

  • FPGAs: Better for evolving standards (e.g., transitioning to 4D LiDAR or neuromorphic radars).
  • ASICs: Tesla’s vertical integration allows tight hardware-software co-design (e.g., optimizing for “BEV transformers”).

Key Takeaways

  1. For Sensor-Agnostic Systems: FPGAs win (used by 90% of LiDAR/radar vendors).
  2. For Vision-Only Scale: Tesla’s ASICs are unbeatable in performance/power.
  3. Hybrid Future: Startups like SiFive are blending FPGA-like programmability with ASIC efficiency.

Tesla’s Lesson: ASICs only make sense if:

  • You control the entire stack (sensors → algorithms).
  • Your volume justifies NRE costs.
  • You’re willing to freeze architectures for 5+ years.

FPGA vs. ASIC Decision Flowchart

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                  ┌───────────────────────┐
                  │ Need sensor flexibility? │
                  └──────────┬─────────────┘
                             │
             ┌───────────────▼────────────────┐
             │                                │
    ┌────────▼───────┐              ┌─────────▼─────────┐
    │ Choose FPGA    │              │ Choose ASIC       │
    │ (Luminar, BMW)│              │ (Tesla, Apple)    │
    └────────────────┘              └───────────────────┘