Memory is a limited resource in embedded AI systems. That makes how tensor memory is allocated and reused an important part of deployment.

In the emmtrix Edge AI Compiler, tensor memory is planned at compile time and assigned to a shared memory pool.

The key is memory reuse: once a tensor is no longer needed, its memory can be reused by another tensor later in the computation.

The animation shows this using a synthetic benchmark: multiple tensor buffers are mapped onto the same memory region over time.

Instead of allocating tensor memory dynamically during inference, the required memory is determined at compile time and reused where possible.

The result: predictable memory usage and a smaller memory footprint for neural network inference.

When every kilobyte counts, memory planning becomes part of the compilation problem.