Talk on Rust adoption at Ather

1. Rust at Ather
Building smart and efficient EVs with Rust.
Because every ounce of memory matters

2. Need for Speed
一 Data generated by each scooter
per day ~ 1GB
一 Close to a half million trips taken
by the riders synced on cloud in
under 15 minutes
一 Number of sensors working
unison for creating intelligence -
50+
一 Trip planner, Smart Eco,
Theft-tow detection, Range
prediction, IMU data processing
and more

3. Talking about data at scale
VEHICLES ON
ROAD
70 k+
LONGEST RIDE
139 km
HOURS SPENT
RIDING
(PER DAY)
105000+
TRIPS
(PER DAY)
450,000
DISTANCE COVERED
(PER DAY)
1,500,000+
km
200+ Million
km ON ROAD

4. 一 Latency in processing data on cloud v/s scooter
一 Limited hardware specs in existing models
一 2GB RAM
一 Quad Core 1.3GHz processor
一 Increased processing cost on cloud
一 Multiple microservices in the constrained hardware environment to control
the peripherals
一 Need for reducing the cost of hardware for overall reduction in the
Bill-of-Materials
一 Need for portability across processors and controllers for a variety of
applications
Where we hit a snag

5. Why we needed a change in tech stack

6. Problems we are after
一 Utilizing memory and CPU
efficiently on edge devices.
一 Add more intelligence on edge.
一 Processing data real time and
sending to cloud with minimal
latency
一 A framework that offers complete
memory security with guaranteed
performance.

7. Why Rust
一 Memory safety at compile time with
very little need for runtime
additions.
一 Lowers TAT of feature roll-out.
一 Good community with a lot of crates
readily available.
一 LLVM compiler infrastructure which
provides same optimizations as that
of C++.
一 Solves for microcontrollers from
memory management at compile
time.
一 Easy cross-compilation

8. Super secret Architecture Diagram
POC
In use

9. - Collects real time data generated by different peripherals.
- Crunch crunch
- de-serialise
- process(no complex operations)
- batch them in-memory
- serialise the data
- publish to cloud.
- Maintains a continuous MQTT connection to the cloud.
- We also use C code within rust for HSM related encryption.
Rust on our scooter

10. - ML packages available in Rust makes matrix & linear algebra computation
easier
- Performance with safety layer which is robust for embedded use cases
- More focus on model building than the reliability of the code
- Allows easier consumption of C-based algo (experimental)
Intelligence with Rust (In evaluation)

11. What do the tests say?

12. Some numbers
Language CPU Memory Execution(s)
Rust 70% 0.6mb 2.43
Go 78% 4.2mb 3.82
Python 100% 8mb 190.72
Test Setup
- RAM: 2GB
- Alg : Multiplication of two 9*9 matrices (because this closely relates to
our algs)
- Processor: Quad Core, single threaded, ARM

13. Some more numbers
Language CPU Memory
Rust 25% 0.4mb
Go 30% 2mb
Test Setup
- RAM: 2GB
- Use Case: ZMQ broker with 2000 messages/sec
- Processor: Quad Core, single threaded, ARM

14. Why does it all matter
- Our Rust application on the vehicle consumes about 3mb - 15mb of RAM.
- The application processes upto 2000 records / sec each ~1kb in size.
- executes 50% faster than Go.
- The memory consumed by Rust is at-least 40-50% lesser.

15. Future Scope
- Migrating more data processing applications on edge.
- Batch processing jobs to aid in data transformations at lower cost and infra.
- Real time processing of Big data on cloud using Rust
- Explore avenue for data conversion for data-lake - json to parquet etc.
- Evaluating scope of identifying least resource chip for cost saving while
moving applications to rust
- KataOS (for microcontrollers i.e. very less memory) to be explored for
memory efficient use-cases

16. Questions?