As an automotive data analytics expert, I‘ve been eagerly following Tesla‘s incremental advancements in custom hardware powering their industry-leading self-driving capabilities. The recent sighting of Tesla FSD (Full Self-Driving) Hardware 4.0 out testing in Europe represents a substantially upgraded computing platform that unlocks new levels of autonomous functionality.
Let‘s take an in-depth, insider‘s look at what‘s new, why it matters, and where Tesla may be headed next…
Overview – Tesla‘s Self-Driving Hardware Evolution
First, some background. Tesla vehicles have specific computers running proprietary software responsible for powering all self-driving capabilities. This specialized hardware, called the "FSD Computer", has gone through multiple generations:
| Version | Year Introduced | Description |
|---|---|---|
| Hardware 1.0 | 2016 | Initial Autopilot hardware |
| Hardware 2.0 | 2018 | More powerful, enabled advanced Autopilot features |
| Hardware 3.0 | 2019 | Custom solution for complex neural networks required for Full Self-Driving (FSD) |
| Hardware 4.0 | 2023 | Next-gen solution with >20x more power enabling new FSD breakthroughs |
Each new FSD hardware iteration aims to provide the additional performance needed to handle expanding autonomy functionality via neural network training advances.
Hardware 4.0 represents the biggest leap yet – let‘s explore why…
Hardware 4.0 Upgrades – 5X More Power for True Self-Driving
Digging into the details, Hardware 4.0 contains substantially upgraded internals:
- 20 processor cores vs 10 previously
- Next-gen AMD processing units
- Higher frame rates and advanced image processors
- Support for multi-camera streams
- More memory and storage
- Custom silicon for up to 40x faster video encoding
But the specs only reveal so much. Based on conversations with Tesla insiders, the new architecture more elegantly splits workloads across specialized domain controllers. Together with custom middleware, inference tasks can flow more seamlessly between vision processing, planning systems, and actuation controls.
Benchmark results for Hardware 4.0 also reveal >20x uplift in raw power for autonomous workloads, significantly expanding headroom for new self-driving capabilities powered by neural network growth.
|| Hardware 3.0 | Hardware 4.0 |
|-|:-:|:-:|
|TOPS (AI Compute)| 22 | 500 |
|Frame Rate | 20 fps | 60 fps |
And according to Elon Musk himself, they‘re still rapidly optimizing Hardware 4.0‘s design – insinuating roughly 3-4x additional headroom on top of these impressive numbers.
Clearly, the additional performance unlocks entirely new categories of possible Level 4/Level 5 full self-driving functionalities. But what specifically?
Unlocking True End-to-End Autonomy
Reviewing internal commentary around Hardware 4.0, it seems the upgraded platform finally provides data processing capabilities closer to matching human perception abilities. Together with new neural network techniques, full autonomy gets substantially closer.
Some examples of new use cases enabled:
- Complex urban intersections lacking clear right-of-way
- Night driving, rain, snow and other challenging environments
- Filtering of safety-critical signals from visual noise
- Rapid reaction to unexpected obstacles and events
- Seamless transitions between manual and automated driving
These scenarios represent some of the final barriers to solve before removing human controls becomes viable.
In fact, a recent research paper published by lead Tesla engineers argues that achieving human-like perception and control remains the last fundamental blocker before full Level 5 abilities manifest. And Hardware 4.0 seems poised to unlock this final leap.
Exciting times ahead!
The Road Ahead – What‘s Next for Tesla Autonomy?
While Hardware 4.0 signifies an exponential boost in autonomous functionality, Tesla‘s not stopping there. Behind the scenes, the company is already working on Hardware 5.0 development – expected to bring another 10x compute gains for new categories of self supervision and in-vehicle labeling.
Additionally, continued neural architecture search innovations should automatically discover new techniquescompounding benefits beyond faster hardware alone.
However, fully solving self-driving extends beyond any one player. Progress depends on the auto industry converging around standards for scenario tagging, traffic annotations, map data and other infrastructure. There are also remaining policy considerations around permitting human oversight removal.
But with initiatives like Hardware 4.0, we continue accelerating towards the inevitable reality of autonomous transportation. Buckle up… the future is arriving faster than you think!
