Machine Learning (ML) algorithms are gaining momentum as main components in a large number of fields, from computer vision and robotics to finance and biotechnology. However, many challenges need to be solved when Artificial Intelligence is applied to different settings, such as cloud computing or embedded systems. At the same time, the use of Field Programmable Gate Arrays (FPGAs) for data-intensive applications is increasingly widespread thanks to the possibility to customize hardware accelerators and achieve high-performance implementations with low energy consumption. This presentation is an overview of the ongoing ML-based projects that are developing at NECSTLab, the laboratory of hardware architectures and computer security of Politecnico di Milano.

1. Machine Learning
Initiative
Luca Stornaiuolo
Dipartimento di Elettronica Informazione e Bioingegneria (DEIB)
luca.stornaiuolo@polimi.it
05/24/2018
NECST @ Uber

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Context Definition
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Context Definition
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Context Definition
Decision Tree Support Vector
Machine
Artificial Neural
Network

5. 5
Context Definition
[1] "Efficient Processing of Deep Neural Networks: A Tutorial and Survey"
Vivienne Sze, Yu-Hsin Chen, Tien-Ju Yang, Joel Emer

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Context Definition
Big Data
Model
New Data
Prediction

7. 7
Context Definition
Big Data
Model
New Data
Prediction
Training
Inference

8. 8
Context Definition
Big Data
Model
Training

9. 9
Context Definition
Big Data
Model
Training

10. 10
Context Definition
Model
New Data
Prediction
Inference

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Context Definition
Model
New Data
Prediction
Inference
Smart Embedded Systems

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Context Definition
Model
New Data
Prediction
Inference
Smart Embedded Systems

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Context Definition
Model
New Data
Prediction
Inference
Smart Embedded Systems

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NECST Research
System Architectures System Security
MaTA
Malware and Threat Analysis
FraudSec
Frauds Analysis and Detection
MoSec
Mobile Security
CyPhy
Security of Cyber-physical systems
DReAMS
Reconfigurable computing and
FPGA-based systems
ORCA
Unleashed Computing Architectures
and Operating Systems
STeEL
Smart Technology Easy Life

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Banksealer
M. Carminati
Framework for banking fraud detection
Models user’s behavior through his/her interaction with
the online banking services to detect fraudulent activities
Behaviors Identification in Social Individuals
G. Muscioni
Develop a hierarchical model to extract behavior at multiple
levels of aggregation (individual behavior, dyadic interactions
and group-level activities)
Exploiting ML @ NECST

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Optimizing ML @NECST
Big Data
Model
New Data
Prediction
Training
Inference

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Pretzel
A. Scolari
Prediction-serving system for scheduling trained ML
models on cloud machines
White box approach
Optimize execution for lower
latency and higher throughput
Sharing operators' common state,
to increase model density per
machine
Optimizing ML for the Cloud

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Optimizing ML for FPGA
GPU
Fixed architecture
High power consumption
Adaptable
ASIC
Fixed architecture
Low Power Consumption
Not adaptable
FPGA
Reconfigurable architecture
Low Power Consumption
Adaptable

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CONDOR
N. Raspa, M. Bacis, G. Natale
Acceleration of Convolutional Neural Network inference
on FPGAs
Cloud Integration
via Amazon F1 Instances
Automatic creation of
an hardware accelerator
for FPGA
Support main deep
learning libraries
FPGA in Datacenters

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FPGA in Embedded Systems
Model
New Data
Prediction
Inference
Smart Embedded Systems

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Deep Learning on PYNQ
L. Stornaiuolo
Framework to help to implement Deep Learning
algorithms on the PYNQ-Z1
Exploits the PYNQ platform
SpiNN
L. Cavinato, E. Migliorini, P. Cancian, M. Arnaboldi
Use Spiking Neural Networks for Reinforcement Learning in
Robotics
Implement efficiently Spiking Neural Networks on FPGAs
SeNSE
P. Cancian, L. Cerina, G. Franco
Accelerate Features Extraction and Classification for
Electromyography-based prostheses on FPGA
Exploits Recurrent Neural Networks for Classification
FPGA in Embedded Systems

22. Machine Learning Initiative
Luca Stornaiuolo
Dipartimento di Elettronica Informazione e Bioingegneria (DEIB)
luca.stornaiuolo@polimi.it
05/24/2018
NECST @ Uber
https://necst.it/
https://www.slideshare.net/necstlab