AI, neural networks and the edge of the cloud: Page 3 of 4

January 04, 2018 //By Francisco Socal
AI, neural networks and the edge of the cloud
Currently, there is much excitement around Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL), as well as other interrelated and emerging technologies.

New use cases

The high performance of dedicated and local hardware acceleration will enable new use cases, for example, in areas such as smart security. The key driver is the reduced total cost of ownership. NNs can, for example, detect suspicious behaviour automatically, raise an alarm, engage operators to monitor the situation and then take action if needed. In addition, the bandwidth to transmit and store surveillance footage is significantly reduced.

Dedicated hardware will also enable security systems to perform on-device analytics, whether in a camera in a city centre, a stadium or a home security system. It has the ability to run multiple different network types, meaning it can enable more intelligent decision making and therefore reduce the number of false-positives, saving time and power. Also, due to the low-power processing, these cameras can be powered over the data network or even be battery operated, making them easier to deploy and manage.

Drones are another great example of AI and NNs working successfully together. They typically fly at speeds in excess of 150mph or 67m/s and therefore, the vision algorithms need to be run locally. Without dedicated hardware for NN acceleration, a drone would need to anticipate obstacles 10-15 meters ahead to avoid a collision. Due to network availability, bandwidth and latency, it is impossible to do this over the cloud. With a true hardware solution, the drone can run multiple NNs to identify and track objects simultaneously, at a distance of only one metre.

As part of my role in Imagination I work on a dedicated hardware offering to enable this required leap in performance and power consumption: the PowerVR Series 2NX neural network accelerator (NNA). Recently, a smartphone manufacturer announced that its hardware used to enable face detection for unlocking the device offered 600 billion operations per second – the Series2NX deliver up to 3.2 trillion operations a second in a single core. Typically, there will be thousands of photos on the smartphone which are sorted automatically in a number of ways, including, for example, identifying all photos will a particular person in them. A GPU could process around 2,400 pictures using one per cent of the battery power but using the same amount of power, the PowerVR Series2NX could handle 428,000 images, highlighting the full potential of NNs.

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