edgefirst-tensor

Zero-copy tensor memory management for edge AI applications.

This crate provides a unified interface for managing multi-dimensional arrays (tensors) with support for different memory backends optimized for ML inference pipelines.

Memory Types

Features

• Automatic memory selection - Tries DMA → SHM → Mem based on availability
• Zero-copy sharing - Share tensors between processes via file descriptors
• Memory mapping - Efficient CPU access to tensor data
• ndarray integration - Optional conversion to/from ndarray::Array (feature: ndarray)

Quick Start

use edgefirst_tensor::{Tensor, TensorMemory, TensorTrait, TensorMapTrait};

// Create a tensor with automatic memory selection
let tensor = Tensor::<f32>::new(&[1, 3, 224, 224], None, None)?;
println!("Memory type: {:?}", tensor.memory());

// Create with explicit memory type
let dma_tensor = Tensor::<u8>::new(&[1920, 1080, 4], Some(TensorMemory::Dma), None)?;

// Map tensor for CPU access
let mut map = tensor.map()?;
map.as_mut_slice().fill(0.0);

// Share via file descriptor (Unix only)
#[cfg(unix)]
let fd = tensor.clone_fd()?;

Platform Support

Feature Flags

• ndarray (default) - Enable ndarray integration for array conversions

Environment Variables

• EDGEFIRST_TENSOR_FORCE_MEM - Set to 1 or true to force heap allocation

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.