Hardware accelerator for video interpretation & neuromorphic computing

Hardware accelerator for video interpretation & neuromorphic computing

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By eeNews Europe

The board enables 16 channels of simultaneous video processing; provides a low power, up to 6x speed boost to BrainChip Studio’s CPU-based Artificial Intelligence Software for Object Recognition; and is claimed to be seven times more efficient than GPU-accelerated (i.e. hosted on graphics process units) deep learning systems.


BrainChip Accelerator is an 8-lane, PCI-Express add-in card that increases the speed and accuracy of the object recognition function of BrainChip Studio software by up to six times, while increasing the simultaneous video channels of a system to 16 per card. The card is low-power and can be installed within existing video surveillance systems without upgrading power systems or thermal management.


BrainChip Studio helps [users such as] law enforcement and intelligence organizations rapidly identify objects in large amounts of archived or live streaming video. By processing multiple video streams simultaneously, the BrainChip Accelerator add-in card enables these organizations to search increasing amounts of video faster, with a higher probability of object recognition and lower total cost of ownership. The system learns from a single low-resolution image, which can be as small as 20 x 20 pixels, and excels in recognition in low-light, low-resolution, noisy environments.


Described as the first commercial implementation of a hardware-accelerated spiking neural network system, BrainChip Accelerator is, “a significant milestone in the development of neuromorphic computing,” a branch of artificial intelligence that simulates neuron functions. The processing is done by six BrainChip Accelerator cores in a Xilinx Kintex Ultrascale field-programmable gate array (FPGA). Each core performs fast, user-defined image scaling, spike generation, and spiking neural network comparison to recognize objects. Scaling images up and down increases the probability of finding objects, and due to the low-power characteristics of spiking neural networks, each core consumes approximately 1W while processing up to 100 frames per second. In comparison to GPU-accelerated deep learning classification neural networks this is claimed to be a 7x improvement of frames/second/Watt.


“There is an estimated four exabytes of video data stored in video surveillance systems,” said Robert Beachler, BrainChip’s Senior Vice President of marketing and business development. “In surveillance, speed and accuracy of analysis are critical concerns for law enforcement and security agencies. The ability of BrainChip Accelerator to process video frames six times faster, while improving the accuracy of object recognition, is a significant force multiplier. It is also a further demonstration of the valuable role that artificial intelligence can now play in these applications.”


Christoph Fritsch, Senior Director for industrial, scientific and medical business at Xilinx, added, “Xilinx is at the forefront of artificial intelligence acceleration. BrainChip’s spiking neural network technology is unique in its ability to provide outstanding performance while avoiding the math intensive, power hungry, and high-cost downsides of deep learning in convolutional neural networks.”


BrainChip Accelerator is compatible with Windows or Linux computing platforms. The company currently supplies it to law enforcement and intelligence agencies as an integrated server appliance; and as an add-in card for security integrators and OEMs.


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