I was reading this blog “Jetson Nano Brings AI Computing to Everyone”
These are the two paragraphs I am confused with. They both are talking about Jetson Nano I guess and they both are very much opposite to each other.
“DNR (did not run) results occurred frequently due to limited memory capacity, unsupported network layers, or hardware/software limitations. Fixed-function neural network accelerators often support a relatively narrow set of use-cases, with dedicated layer operations supported in hardware, with network weights and activations required to fit in limited on-chip caches to avoid significant data transfer penalties. They may fall back on the host CPU to run layers unsupported in hardware and may rely on a model compiler that supports a reduced subset of a framework (TFLite, for example).”
“Jetson Nano’s flexible software and full framework support, memory capacity, and unified memory subsystem, make it able to run a myriad of different networks up to full HD resolution, including variable batch sizes on multiple sensor streams concurrently. These benchmarks represent a sampling of popular networks, but users can deploy a wide variety of models and custom architectures to Jetson Nano with accelerated performance. And Jetson Nano is not just limited to DNN inferencing. Its CUDA architecture can be leveraged for computer vision and Digital Signal Processing (DSP), using algorithms including FFTs, BLAS, and LAPACK operations, along with user-defined CUDA kernels.”
My question is… “Does Jetson Nano output these DNR results because of limited memory capacity on these algorithms?”