It’s getting harder and harder to squeeze more performance out of your phone’s camera hardware. That’s why companies like Google are turning to computational photography: using algorithms and machine learning to improve your snaps. The latest research from the search giant, conducted with scientists from MIT, takes this work to a new level, producing algorithms that are capable of retouching your photos like a professional photographer in real time, before you take them.
 Quantized Neural Networks for FPGA InferenceSponsored by Intel Implementing a low precision network in FPGA hardware for efficient inferencing provides numerous advantages when it comes to meeting demanding specifications. The increased flexibility allows optimization of throughput, overall power consumption, resource usage, device size, TOPs/watt, and deterministic latency. These are important benefits where scaling and efficiency are inherent requirements of the application. |
