Month: September 2016

Neural Network Training and Inferencing on Nvidia

Nvidia just announced the Tesla P40 and P4 cards for Neural network inferencing applications. A review is at Comparing it to the Tesla P100 released earlier this year, the P40 is targeted to inferencing applications. Whereas the P100 was targeted to more demanding training phase of neural networks. P4o comes with the TensorRT (real time) library for fast inferencing (e.g. real time detection of objects).

Some of the best solutions of hard problems in machine learning come from neural networks, whether in computer vision, voice recognition, games such as Go and other domains. Nvidia and other hardware kits are accelerating AI applications with these releases.

What happens if the neural network draws a bad inference, in a critical AI application ? Bad inferences have been discussed in the literature, for example in the paper: Intriguing properties of neural networks.

There are ways to minimize bad inferences in the training phase, but not foolproof – in fact the paper above mentions that bad inferences are low probabalility yet dense.

Level 5 autonomous driving is where the vehicle can handle unknown terrain. Most current systems are targeting Level 2 or 3 autonomy. The Tesla Model S’ Autopilot is Level 2.

An answer is to pair it with a regular program that checks for certain safety constraints. This would make it safer, but this alone is likely insufficient either for achieving Level 5 operations, or for providing safely for them.

Automotive and Process Safety Standards

ISO 26262 is a standard for Automotive Electric/Electronic Systems safety, that is adopted by car manufacturers. Its V shape consists of two legs, the first comprising definition, analysis, design, architectural design, development and implementation. The second leg consists of verification and validation of the software, starting from unit tests to functional tests, safety tests and system-wide tests. Model based design is used to reduce the complexity. These models are now fairly complex. Model based design is the one of the value adds that Mentor Graphics automotive kit provides is help with achieving compliance with this standard.

ISO 26262 is derived from its parent, the IEC 61508 standard, which is titled Functional Safety of Electrical/Electronic/Programmable Electronic Safety-related Systems. This parent standard has variants for safety of automotive, railway, nuclear, manufacturing processes (refineries, petrochemical, chemical, pharmaceutical, pulp and paper, and power) and machinery related electrical control systems. An associated, upcoming standard is the SAE J2980.

An excellent talk today by MIT fellow Ricardo Dematos discussed more comprehensive approaches to automotive safety. This is building up from his work with safety research at MIT, AWS IoT and our SyncBrake entry for V2V safety at TechCrunch Disrupt 2015.