Gian Marco Iodice

Gian Marco Iodice

Greater Cambridge Area
2K followers 500+ connections

About

Gian Marco Iodice is an edge and mobile computing specialist at Arm, focused on machine…

Activity

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Experience

  • Arm Graphic

    Arm

    Cambridge, England, United Kingdom

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    United Kingdom

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    Cambridge, United Kingdom

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    Cambridge, United Kingdom

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    Cambridge, United Kingdom

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    Cambridge, United Kingdom

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    Cambridge, United Kingdom

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    Milan Area, Italy

Education

  • Università di Pisa Graphic

    Università di Pisa

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    Exams list:

    Microelectronic system design: 28/30
    Image processing on bio-images: 29/30
    Embedded system design HW/SW co-design on FPGA: 29/30
    Operating system and multi-thread programming: 30/30
    Internet of things (IoT) (Ambient Intelligence): 29/30
    Digital circuit design: 30/30
    Opto-Electronics: 30/30
    MEMS design: 25/30
    Power electronics: 27/30
    Wireless Systems Electronics: 30/30
    Analog electronics: 26/30
    Mixed signal design: 28/30

    Thesis:…

    Exams list:

    Microelectronic system design: 28/30
    Image processing on bio-images: 29/30
    Embedded system design HW/SW co-design on FPGA: 29/30
    Operating system and multi-thread programming: 30/30
    Internet of things (IoT) (Ambient Intelligence): 29/30
    Digital circuit design: 30/30
    Opto-Electronics: 30/30
    MEMS design: 25/30
    Power electronics: 27/30
    Wireless Systems Electronics: 30/30
    Analog electronics: 26/30
    Mixed signal design: 28/30

    Thesis: “Implementation of Real-Time Dense Stereo Vision for augmented Reality in ARM GPUs”
    Master thesis project: 30/30

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    Thesis: "WAV audio player on ARM Cortex-M3 with PWM (TI Stellaris 8962)" 30/30 cum Laude
    Link: https://www.youtube.com/watch?v=tyqI4rbx7zU

Licenses & Certifications

  • Arm Expert - Developer program Graphic

    Arm Expert - Developer program

    Arm

    Issued
  • Certificate of Completion "Rapid Development with Atmel AVR XMEGA and Atmel AVR Studio 5: Hands on Training" Graphic

    Certificate of Completion "Rapid Development with Atmel AVR XMEGA and Atmel AVR Studio 5: Hands on Training"

    Atmel Corporation

    Issued

Publications

Projects

  • ARM Compute library: ARM Computer Vision & Machine Learning library for both ARM CPUs and ARM GPUs

    The ARM Compute Library is a collection of low-level functions optimized for ARM CPU and GPU architectures targeted at image processing, computer vision, and machine learning. It is available free of charge under a permissive MIT open source license.

    See project
  • Using SGEMM and FFTs to Accelerate Deep Learning - Presentation at the May 2016 Embedded Vision Summit

    Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation we will discuss and analyze how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNNs)…

    Matrix Multiplication and the Fast Fourier Transform are numerical foundation stones for a wide range of scientific algorithms. With the emergence of deep learning they are becoming even more important, particularly as use cases extend into mobile and embedded devices. In this presentation we will discuss and analyze how these two key, computationally-intensive algorithms can be used to gain significant performance improvements for convolutional neural network (CNNs) implementations.

    After a brief introduction to the nature of CNN computations, we will explore the use of GEMM (General Matrix Multiplication) and mixed-radix FFTs to accelerate 3D convolution. We’ll show examples of OpenCL implementations of these functions and highlight their advantages, limitations and trade-offs. Central to the techniques explored will be emphasis on cache-efficient memory accesses and the crucial role of reduced-precision data types.

    http://www.embedded-vision.com/summit/using-sgemm-ffts-accelerate-deep-learning
    https://www.youtube.com/watch?v=pvuCg2yT5wY

    See project
  • Real-time Dense Passive Stereo Vision: Optimizing Computer Vision Applications Using OpenCL on ARM

    The presentation was part of the Computer Vision on ARM Seminar held on 11 May, 2015 at the Santa Clara Convention Centre (US)
    http://community.arm.com/docs/DOC-10303
    https://www.youtube.com/watch?v=hRFVUBBYNas
    Passive stereo vision is a powerful visual sensing technique aimed at inferring depth without using any structured light. Nowadays, as it offers low cost and reliability solutions, it finds application in many real use cases, such as natural user interfaces, industrial…

    The presentation was part of the Computer Vision on ARM Seminar held on 11 May, 2015 at the Santa Clara Convention Centre (US)
    http://community.arm.com/docs/DOC-10303
    https://www.youtube.com/watch?v=hRFVUBBYNas
    Passive stereo vision is a powerful visual sensing technique aimed at inferring depth without using any structured light. Nowadays, as it offers low cost and reliability solutions, it finds application in many real use cases, such as natural user interfaces, industrial automation, autonomous vehicles, and many more. Since stereo vision algorithms are extremely computationally expensive, resulting in very high CPU load, the aim of this project is to demonstrate the feasibility of this task on a low power mobile ARM® Mali™ GPU. In particular, the implementation uses a local stereo vision method based on a novel extension of census transform, which exploits the highly parallel execution feature of mobile Graphic Processing Units with OpenCL.
    The presentation (https://www.youtube.com/watch?v=hRFVUBBYNas) shows up the approaches and the strategies used to optimize the OpenCL™ code in order to reach significant performance benefits on the GPU.

    See project
  • MP3 Player with scrolling menu on ARM® Cortex™-M3 (Beatstream 2.0)

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    Beatstream 2.0:
    The Beatstream project started in July 2011 and is an hobby project.
    Some videos related to Beatstream 1.0 (wav player) are available on my youtube channel.

    Beatstream 2.0 was developed in March 2012 as upgrade of the previous implementation Beatstream 1.0.

    The project was sped over on ARM Cortex-M3 (STM32F1x MCU @72MHz, 512KByte Program Memory and 64KByte Data Memory).

    LCD controller: ILI9325
    Touch Screen controller: TSC2046 (SPI…

    Beatstream 2.0:
    The Beatstream project started in July 2011 and is an hobby project.
    Some videos related to Beatstream 1.0 (wav player) are available on my youtube channel.

    Beatstream 2.0 was developed in March 2012 as upgrade of the previous implementation Beatstream 1.0.

    The project was sped over on ARM Cortex-M3 (STM32F1x MCU @72MHz, 512KByte Program Memory and 64KByte Data Memory).

    LCD controller: ILI9325
    Touch Screen controller: TSC2046 (SPI protocol)
    MP3/WMA decoder: VS1003

    It was used the SDIO periph. to inferface the microSD card to MCU.

    The MP3 player works without O.S.

    Beatstream 2.0 includes new features with respect to Beatstream 1.0 such as the Mp3/WMA decoder. It was developed from scratch in C with the only exception of FAT32 module where it was used the elm-chan FATFS (http://elm-chan.org/fsw/ff/00index_e.html).

    For the Beatstream 2.0 it was developed as well a new GUI. The GUI has a fast and responsive scrolling menu which is perfect for touchscreen devices. The scroll is composed by click (or touch) & drag scrolling with an additional movement after the finger is lifted off the screen.

    See project

Languages

  • Italian

    Native or bilingual proficiency

  • English

    Professional working proficiency

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