Safety-Related Challenges and Opportunities for GPUs in the Automotive Domain

Alcaide, Sergi; Kosmidis, Leonidas; Tabani, Hamid; Hernández, Carles; Abella, Jaume; Cazorla, Francisco J.

doi:10.1109/mm.2018.2873870

Cited by 25 publications

(25 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, we can proceed to decode the Posit. The associated real value is therefore −256 1 • 2 1 • (1 + 39/512). The final value is therefore −512 • (1 + 39/512) = −551 (exact value, i.e., no rounding, for this case).…”

Section: Posit Arithmeticmentioning

confidence: 99%

“…The step in Equation (25) is always L1 for esbits = 0 thanks to fast Sigmoid approximation. The result of this step is always on [1,2]. The step in Equation (26) is always L1, and the output is on [1/2, 1] ∈ [0, 1].…”

Section: Hyperbolic Tangent Extended Linear Unit and Their Approximmentioning

confidence: 99%

“…However, these solutions are considerable demanding in terms of resources. Moreover, adding parallelization in critical systems may reduce the predictability of the said system (see References [1,2]). Furthermore, even the use of floating point SIMD engines is not always possible in embedded systems (e.g., ARM Cortex-M4 [3]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Fast Approximations of Activation Functions in Deep Neural Networks when using Posit Arithmetic

Cococcioni

Rossi

Ruffaldi³

et al. 2020

Sensors

View full text Add to dashboard Cite

With increasing real-time constraints being put on the use of Deep Neural Networks (DNNs) by real-time scenarios, there is the need to review information representation. A very challenging path is to employ an encoding that allows a fast processing and hardware-friendly representation of information. Among the proposed alternatives to the IEEE 754 standard regarding floating point representation of real numbers, the recently introduced Posit format has been theoretically proven to be really promising in satisfying the mentioned requirements. However, with the absence of proper hardware support for this novel type, this evaluation can be conducted only through a software emulation. While waiting for the widespread availability of the Posit Processing Units (the equivalent of the Floating Point Unit (FPU)), we can already exploit the Posit representation and the currently available Arithmetic-Logic Unit (ALU) to speed up DNNs by manipulating the low-level bit string representations of Posits. As a first step, in this paper, we present new arithmetic properties of the Posit number system with a focus on the configuration with 0 exponent bits. In particular, we propose a new class of Posit operators called L1 operators, which consists of fast and approximated versions of existing arithmetic operations or functions (e.g., hyperbolic tangent (TANH) and extended linear unit (ELU)) only using integer arithmetic. These operators introduce very interesting properties and results: (i) faster evaluation than the exact counterpart with a negligible accuracy degradation; (ii) an efficient ALU emulation of a number of Posits operations; and (iii) the possibility to vectorize operations in Posits, using existing ALU vectorized operations (such as the scalable vector extension of ARM CPUs or advanced vector extensions on Intel CPUs). As a second step, we test the proposed activation function on Posit-based DNNs, showing how 16-bit down to 10-bit Posits represent an exact replacement for 32-bit floats while 8-bit Posits could be an interesting alternative to 32-bit floats since their performances are a bit lower but their high speed and low storage properties are very appealing (leading to a lower bandwidth demand and more cache-friendly code). Finally, we point out how small Posits (i.e., up to 14 bits long) are very interesting while PPUs become widespread, since Posit operations can be tabulated in a very efficient way (see details in the text).

show abstract

Section: Posit Arithmeticmentioning

confidence: 99%

Section: Hyperbolic Tangent Extended Linear Unit and Their Approximmentioning

confidence: 99%

See 1 more Smart Citation

Fast Approximations of Activation Functions in Deep Neural Networks when using Posit Arithmetic

Cococcioni

Rossi

Ruffaldi³

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The effectiveness and scalability of traditional Verification and Validation (V&V) approaches are threatened by the complexity and unboundedness of the input and result spaces of functionalities such as perception and tracking [55,6]. The untenable number of potential inputs from the operational environment, and the non-deterministic nature of decision-making algorithms, complicate the definition of worst-case scenarios in both functional and non-functional dimensions [55].…”

Section: Introductionmentioning

confidence: 99%

Timing of Autonomous Driving Software: Problem Analysis and Prospects for Future Solutions

Alcon

Tabani

Kosmidis

et al. 2020

2020 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS)

Self Cite

View full text Add to dashboard Cite

The software used to implement advanced functionalities in critical domains (e.g. autonomous operation) impairs software timing. This is not only due to the complexity of the underlying high-performance hardware deployed to provide the required levels of computing performance, but also due to the complexity, non-deterministic nature, and huge input space of the artificial intelligence (AI) algorithms used. In this paper, we focus on Apollo, an industrial-quality Autonomous Driving (AD) software framework: we statistically characterize its observed execution time variability and reason on the sources behind it. We discuss the main challenges and limitations in finding a satisfactory software timing analysis solution for Apollo and also show the main traits for the acceptability of statistical timing analysis techniques as a feasible path. While providing a consolidated solution for the software timing analysis of Apollo is a huge effort far beyond the scope of a single research paper, our work aims to set the basis for future and more elaborated techniques for the timing analysis of AD software.

show abstract

“…The answer to this performance demand has been the deployment of accelerators along with the microcontrollers, being Graphic Processing Units (GPUs) the main representative of those [17], [28], [24] despite the existing challenges using GPUs in this domain [29], [5]. In particular, several products such as Renesas R-Car H3 [1], NVIDIA Jetson TX2 [18] and NVIDIA Jetson Xavier [26] have already reached the market building upon GPU technology inherited from the high-performance domain.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Optimization of Automotive GPUs

Mazzocchetti

Benedicte

Tabani

et al. 2019

2019 31st International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

Self Cite

View full text Add to dashboard Cite

Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) have drastically increased the performance demands of automotive systems. Suitable highperformance platforms building upon Graphic Processing Units (GPUs) have been developed to respond to this demand, being NVIDIA Jetson TX2 a relevant representative. However, whether high-performance GPU configurations are appropriate for automotive setups remains as an open question. This paper aims at providing light on this question by modelling an automotive GPU (Jetson TX2), analyzing its microarchitectural parameters against relevant benchmarks, and identifying specific configurations able to meaningfully increase performance within similar cost envelopes, or to decrease costs preserving original performance levels. Overall, our analysis opens the door to the optimization of automotive GPUs for further system efficiency.

show abstract

Safety-Related Challenges and Opportunities for GPUs in the Automotive Domain

Cited by 25 publications

References 5 publications

Fast Approximations of Activation Functions in Deep Neural Networks when using Posit Arithmetic

Fast Approximations of Activation Functions in Deep Neural Networks when using Posit Arithmetic

Timing of Autonomous Driving Software: Problem Analysis and Prospects for Future Solutions

Performance Analysis and Optimization of Automotive GPUs

Contact Info

Product

Resources

About