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FPGA-based Neuromorphic Accelerator board recognizes objects 7x more efficiently than GPUs on GoogleNet, AlexNet

Xilinx Employee
Xilinx Employee
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BrainChip Holdings has just announced the BrainChip Accelerator, a PCIe server-accelerator card that simultaneously processes 16 channels of video in a variety of video formats using spiking neural networks rather than convolutional neural networks (CNNs). The BrainChip Accelerator card is based on a 6-core implementation BrainChip’s Spiking Neural Network (SNN) processor instantiated in an on-board Xilinx Kintex UltraScale FPGA.

 

Here’s a photo of the BrainChip Accelerator card:

 

 

BrainChip FPGA Board.jpg 

 

BrainChip Accelerator card with six SNNs instantiated in a Kintex UltraScale FPGA

 

 

 

Each BrainChip core performs fast, user-defined image scaling, spike generation, and SNN comparison to recognize objects. The SNNs can be trained using low-resolution images as small as 20x20 pixels. According to BrainChip, SNNs as implemented in the BrainChip Accelerator cores excel at recognizing objects in low-light, low-resolution, and noisy environments.

 

The BrainChip Accelerator card can process 16 channels of video simultaneously with an effective throughput of more than 600 frames per second while dissipating a mere 15W for the entire card. According to BrainChip, that’s a 7x improvement in frames/sec/watt when compared to a GPU-accelerated CNN-based, deep-learning implementation for neural networks like GoogleNet and AlexNet. Here’s a graph from BrainChip illustrating this claim:

 

 

 

BrainChip Efficiency Chart.jpg 

 

 

 

 

SNNs mimic human brain function (synaptic connections, neuron thresholds) more closely than do CNNs and rely on models based on spike timing and intensity. Here’s a graphic from BrainChip comparing a CNN model with the Spiking Neural Network model:

 

 

 

 

BrainChip Spiking Neural Network comparison.jpg 

 

 

For more information about the BrainChip Accelerator card, please contact BrainChip directly.