Simpleperf case study: Fast initialization of TFLite’s Memory Arena

Posted by Alan Kelly , Software Engineer One of our previous articles, Optimizing TensorFlow Lite Runtime Memory , discusses how TFLite’s memory arena minimizes memory usage by sharing buffers between tensors. This means we can run models on even smaller edge devices. In today’s article, I will describe the performance optimization of the memory arena initialization so that our users get the benefit of low memory usage with little additional overhead.