This document discusses implementing neural networks on FPGAs for smart vehicles. FPGAs provide benefits for embedded applications by reducing computational complexity, memory usage, and power consumption compared to other platforms. The key benefits are that FPGAs allow shift bit multiplication using LUTs, fixed point operations using DSPs, and entire neural network models and parameters can be stored in BRAMs. This enables running neural networks for tasks like computer vision with a high level of parallelism.

1. FPGA based systems for
automotive: the benefits
FPGA implementation of Neural
Networks for smart vehicles
Matteo Fettizio: matteo.fettizio@mail.polimi.it
Amin Machkouk: amin.machkouk@mail.polimi.it
Soufiane Machkouk: soufiane.machkouk@mail.polimi.it
Sala seminari DEIB, edificio
20
03/04/2019

2. The smarter, the better
 The development of new electronic
systems in the automotive industry is
mostly aimed at making our vehicles
smarter
 Comfort, safety and efficiency can
best be provided if the vehicle can
learn from its enviroment and take
decisions based on it
Neural Networks

4. Embedded systems application
Running a Convolutional
Neural Network means:
 High computational
power
 High memory
occupation
 High power
consumption
Embedded
systems

5. The solution : quantization
Weights, inputs and outputs get quantized on two levels
N bit quantization
CLB DSP

6. The matrix-vector threshold unit
 Sliding Window unit(SWU):
convolution to matrix-
matrix multiplications
 Processing element(PE) :
scalar products
computation and activation
 Single instruction multiple
data (SIMD) : PE operations
control

7.  Shift bit multiplication
computable in LUTs,
DSPs deal with fixed
point operations
 Model’s parameters
entirely storable in
BRAMs
 High level of parallelism
is reachable
FPGA’s benefits
Perfect for embedded
applications
Reduction of
computational
complexity,
memory and
power

8. Contact us
Neuronsonwheelsatnects
Neuronsonwheelsatnects
Neuronsonwheelsatnects@gmail.com