The PCIe Gen.3 card is for use as a hardware accelerator for applications developed within the previously released BrainChip Studio, a software product that was pitched at vision in surveillance, security and defense applications.
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 a factor of six, while increasing the simultaneous video channels monitored to up to 16 per card.
BrainChip's approach to neural networks differs from other companies offering artificial neural networks, in that BrainChip models the brain and its neuron and synapse architecture more closely and includes models of spiking signals and connection reinforcement and connection inhibition. Bob Beachler, senior vice president of marketing and business development, said that this makes BrainChip the first company with a commercial offering of a true neuromorphic hardware, albeit implemented in a Xilinx, Kintex Ultrascale 115 chip.
The spiking neural network approach does not require the protracted, usually cloud-based training programs, required for convolution neural networks and even allows "one-shot" training and self-learning procedures. One of the things the spiking neural network (SNN) excels at is finding patterns in noisy environments with low computational and energy requirements, Beachler said.
For applications such as facial recognition this does require the application of some conventional computer graphics pre-processing to perform scaling and define a region of interest within a scene.
For now the hardware is being offered to accelerate BrainChip Studio which has been deployed with law enforcement and intelligence organizations to rapidly identify objects in large amounts of archived or in 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.
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