TDA2X, a SoC optimized for advanced driver assistance systems

Advanced driver assistance systems applications increasingly use cameras and image processing algorithms. To embed and achieve real-time execution of these algorithms, semiconductor companies propose heterogeneous systems-on-chip (SoCs). Embedding algorithms on this type of hardware is not trivial: One needs to determine how to partition the computational load on the different processing units. In addition, it is not easy to predict whether a given algorithm can be executed on a given heterogeneous SoC while meeting real-time constraints. We propose a novel global methodology to assist with embedding image processing algorithms on heterogeneous SoC while meeting realtime constraints (using a soft real-time analysis). Our approach proposes several heuristics predicting delays and execution times and is based on a set of multi-level test vectors which extract key features of heterogeneous architectures.

show abstract

“…The TDA2x [28] is composed of four different types of programmable units. First, it provides a 750 MHz dual-core ARM A15 and a dual-Cortex-M4.…”

Section: Texas Instruments Tda2xmentioning

confidence: 99%

A novel global methodology to analyze the embeddability of real-time image processing algorithms

Saussard

Bouzid

Vasiliu

et al. 2017

J Real-Time Image Proc

View full text Add to dashboard Cite

show abstract

“…The TDA2x [6] is composed of four different types of programmable units. First, it provides a 750 MHz dual-core ARM A15 and a dual-Cortex-M4.…”

Section: Texas Instruments Tda2xmentioning

confidence: 99%

A Robust Methodology for Performance Analysis on Hybrid Embedded Multicore Architectures

Saussard

Bouzid

Vasiliu

et al. 2016

2016 IEEE 10th International Symposium on Embedded Multicore/Many-Core Systems-on-Chip (MCSOC)

View full text Add to dashboard Cite

Today's vehicles increasingly embed software intelligence in order to be safer for the driver, and to achieve autonomous driving in a close future. To answer the computational needs of these algorithms, system-on-chip (SoC) suppliers propose heterogeneous architectures. With such complex SoCs, embedding applications in vehicle becomes more and more complex for car manufacturers. Indeed, it is not trivial to find the best suited SoC for a given application, and to define load balancing strategies when working with heterogeneous architectures. These difficulties can be overcome by using performance prediction, based on computing architectures models. To build these models, we provide a set of test vectors which automatically extract key characteristics of tested architectures. Our methodology is able to perform a complete computing architecture model, by using 3 different levels of tests, each one characterizing a specific situation representative of real applications. We aim to obtain performance prediction for different applications, for any embedded SoCs based on models performed with this methodology. In this paper, we describe our characterization methodology, and show results obtained with embedded SoCs used for automotive applications.

show abstract

“…Our next hardware target for studying ADAS algorithms embeddability is the TDA2x platform [5]. Gradient will probably be computed by the DSP, and IPM and Hough transform by the EVE vectorial processors.…”

Section: Hough Transformmentioning

confidence: 99%

“…Heterogeneous architectures embed several processing units with different capabilities on the same System on Chip (SoC), often with massively parallel computing unit. We can cite the Tegra K1 SoC of Nvidia (which embed ARM, GPU and an ISP), the TDA2x SoC [5] of Texas Instrument (ARM, DSP and EVE vectorial processor). This type of SoC offers a good solution for embedding image processing algorithms in the automotive field because it can handle high performance computing with low-power consumption.…”

Section: Introductionmentioning

confidence: 99%

The embeddability of lane detection algorithms on heterogeneous architectures

Saussard

Bouzid

Vasiliu

et al. 2015

2015 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Lane detection plays a crucial role for Advanced Driver Assitance System (ADAS) or autonomous driving applications. Literature shows a lot of lane detection algorithms can work in real time with good results. However, they require much computer processing and cannot be embedded in a vehicle ECU without deep software optimizations. In this paper, we discuss the embeddability of lane detection algorithms by comparing state-of-the-art algorithms in terms of functional performance and computational timing. We identify what essential parts of lane detection are time consuming, and show these parts can be computed in real time on embedded systems.

show abstract

TDA2X, a SoC optimized for advanced driver assistance systems

Cited by 25 publications

References 2 publications

A novel global methodology to analyze the embeddability of real-time image processing algorithms

A novel global methodology to analyze the embeddability of real-time image processing algorithms

A Robust Methodology for Performance Analysis on Hybrid Embedded Multicore Architectures

The embeddability of lane detection algorithms on heterogeneous architectures

Contact Info

Product

Resources

About