NMAX Performance on YOLOv3 Real Time Object Recognition
YOLOv3 is a Neural Network model, with more than 100 stages, that does real time object recognition. It can process images of any size. It has 62 Million weights For high resolution, 2 Megapixels (2048 x 1024), each image requires 400 billion MACs = 800 billion operations! This is 100x the computational requirement for ResNet-50!
Real time object recognition is of high interest for a range of applications such as: surveillance cameras that can recognize people and differentiate between family/friends vs strangers; backup cameras that want to avoid kids and bikes; and autonomous driving at highway speeds.
Below is a table of interest to companies doing autonomous driving. The 6x6 NMAX array column runs YOLOv3 on 2 Megapixel images for 1 camera at ~30 frames/second; 12x6 for two cameras; 12x12 for four cameras. We can’t compare to others, because no one else has provided YOLOv3 performance.
Things to note in this table:
some of the configurations require more memory for optimal performance than ResNet-50: YOLOv3 is a bigger model,
the throughput is roughly linear: about doubles for every doubling of NMAX resources. NMAX is a scalable architecture,
MAC utilization is again very high at 50-70% for batch=1 (utilization is less than ResNet-50 because there are more weights and the images being processed are much larger),
DRAM bandwidth is very low; other architectures require 100’s of GBs/second and 4x or more DRAMs. This is possible because SRAM bandwidth is huge: this is due to the distributed nature of processing and the interconnect technologies.
Again, NMAX is scalable to lower performance too: a 1x1 NMAX array processes 1 frame/second, perhaps sufficient for applications like backup or front door surveillance cameras. NMAX is modular, contact us at email@example.com to learn about the NMAX configuration that meets your throughput requirement.
Performance also scales across image sizes: for example, a 1 Megapixel image will process at twice the frame rate of the 2 Megapixel example above.