Modern deep learning models, such as large language models (LLMs), are heavily constrained by memory bandwidth. GPUs can execute floating-point operations (FLOPs) much faster than they can fetch weights from memory. For instance, an NVIDIA A10 has a peak computation throughput of 125 TFLOPS and a memory bandwidth of 600GB/s.

We started with a compilation time of 108 seconds for the matmul benchmarks, which was reduced to only 1 second after all the optimizations. The most effective optimization was the element-type generics swap, where we instantiated generic functions with predefined "faked" element types to reduce the amount of LLVM code generated. The second optimization also had a major impact, further reducing the compilation time by nearly 3×. This was achieved by using our comptime system instead of associated const generics to represent the matmul instruction sizes. Finally, the last optimization—also the simplest—was to reduce the LLVM optimization level to zero, which is particularly useful for debug builds, such as tests.

This release brings major performance improvements to tensor operations, particularly in matrix multiplication and convolution, along with experimental ROCm/HIP and SPIR-V support enabled by CubeCL runtimes. It also introduces foundational features for multi-backend compatibility and adds new quantization operations.

Modern deep learning models, such as large language models (LLMs), are heavily constrained by memory bandwidth. GPUs can execute floating-point operations (FLOPs) much faster than they can fetch weights from memory. For instance, an NVIDIA A10 has a peak computation throughput of 125 TFLOPS and a memory bandwidth of 600GB/s.

2024 marked a significant evolution in Burn's architecture. Traditional deep learning frameworks often require developers to compromise between performance, portability, and flexibility; we aimed to transcend these trade-offs. Looking ahead to 2025, we are committed to applying this philosophy across the entire computing stack, encompassing everything from embedded devices to data centers.

In the rapidly evolving landscape of artificial intelligence, one truth stands paramount: size matters. However, the future of AI shouldn't be constrained by hardware monopolies or software limitations, and this is where Burn and CubeCL come in.

We started with a compilation time of 108 seconds for the matmul benchmarks, which was reduced to only 1 second after all the optimizations. The most effective optimization was the element-type generics swap, where we instantiated generic functions with predefined "faked" element types to reduce the amount of LLVM code generated. The second optimization also had a major impact, further reducing the compilation time by nearly 3×. This was achieved by using our comptime system instead of associated const generics to represent the matmul instruction sizes. Finally, the last optimization—also the simplest—was to reduce the LLVM optimization level to zero, which is particularly useful for debug builds, such as tests.

This post explores Burn's tensor operation stream strategy, optimizing models through an eager API by creating custom kernels with fused operations. Our cusotm GELU experiment reveals a remarkable improvement of up to 78 times on our WGPU backend.

Crafting high-performance GPU kernels for common deep learning operations, such as matrix multiplication (matmul) and reduction, requires finesse. The speed of these kernels varies depending on input shapes and the GPU device in use, meaning the fastest one may change based on the context. In Burn, Autotune automates the task of dynamically performing kernel selection, allowing one to create a plethora of kernel variations with confidence that the best-performing one will be executed in every situation.

Developing new high-performance deep learning backends in Burn has become remarkably easy, as it can be readily enhanced with advanced capabilities such as asynchronous computations, intelligent memory management, and autotuning mechanisms. The innovative Burn-Compute crate lays the architectural foundation for in-house backends, effortlessly equipping them with advanced features to maximize efficiency.

Introducing Burn's new Cross-Platform GPU Backend built using WGPU. Burn now supports running deep learning models on a variety of hardware configurations, leveraging graphics APIs such as Vulkan, DirectX 11/12, Metal, OpenGL, and WebGPU. We discuss the possible applications in various domains and glimpse into the promising future of the framework.

The latest release of Burn includes significant changes to its memory management strategy, and tensor-allocated memory can now be reused way more often. Overall, these changes significantly reduce memory usage, especially on the CPU compared to PyTorch.

Burn provides key components that serve as the building blocks of the framework and your deep learning projects. The first entry in the Building Blocks series explores the dataset and batcher traits, and how they fit into Burn's data loading process.

In this updated tutorial, we'll implement the popular ResNet family of models and import ImageNet pre-trained weights available online.

This release brings major performance improvements to tensor operations, particularly in matrix multiplication and convolution, along with experimental ROCm/HIP and SPIR-V support enabled by CubeCL runtimes. It also introduces foundational features for multi-backend compatibility and adds new quantization operations.

This release brings major performance improvements to tensor operations, particularly in matrix multiplication and convolution, along with experimental ROCm/HIP and SPIR-V support enabled by CubeCL runtimes. It also introduces foundational features for multi-backend compatibility and adds new quantization operations.

This release marks the debut of our CubeCL integration, which brings cross-platform GPU programming capabilities directly to Rust. As always, it also includes numerous bug fixes, performance enhancements, new tensor operations, and improved documentation.

Burn 0.13 introduces major performance enhancements, new tensor operations, improved autodiff, Just-in-Time backend refactoring, and numerous feature additions across modules, optimizers, and backends.