ETL Version 1.2.1

• Feature Support for embeddings and embedding gradients
• Feature Support for merging matrices together
• Feature Support for bias_batch_var_2d
• Feature Support for dropout masks
• Feature Support for normalization
• Performance Vectorize hyperbolic functions
• Performance Advanced GPU patterns detections
• Performance Asynchronous GPU computation
• GPU Support for uniform and normal random generators
• GPU Support for shuffle operations
• Bug Fix fast_dyn_matrix with bool
• Bug Fix possible stack overflow with fast matrix and aliasing
• Bug Correctly handle aliasing in assignable (sub_view for instance)
• Bug Fix small compilation bug with sub_matrix
• Bug Fix CPU/GPU consistency bug with iterators
• Bug Fix bug with GPU convolution flipping

ETL Version 1.2

• Feature GPU support for basic expressions (such as c = 1.0 * b + d + e - 1.0)
• Feature GPU Support for unary and binary operators
• Feature Support for convolutions for matrices of different data types
• Feature Support for log2 / log10
• Feature Default selection of algorithms by default
• Feature Support for categorical cross entropy loss and error
• Feature Improve support for complex numbers and etl::complex
• Performance Improved performance of using parallel BLAS
• Misc Full cleanup of the traits
• Misc Use of variable templates (C++14) for the traits
• Misc Improved support for clang
• Misc Reduced compilation time for non-tests / non-benchmark code
• Misc Reduce durations of the tests
• Misc Preliminary C++17 if constexpr support
• Bug Fix bug in the GEMM kernel for CM = CM * CM
• Bug Vectorization bug for binary operations with different data types
• Bug GPU memory was not correctly handled when std::move is used

ETL Version 1.1

• Performance Better dispatching for alignment
• Performance Much faster multiplications between matrices of different major
• Performance Highly improved performed of multiplications with transpose
• Performance Vectorization of signed integer operations
• Performance Faster CPU convolutions
• Performance Better parallelization of convolutions
• Performance Much better GEMM/GEMV/GEVM kernels (when BLAS not available)
• Performance Reduced overhead for 3D/4D matrices access by indices
• Performance Use of non-temporal stores for large matrices
• Performance Forced alignment of matrices
• Performance Force basic padding of vectors
• Performance Better thread reuse
• Performance Faster dot product
• Performance Faster batched outer product
• Performance Better usage of FMA
• Performance SSE/AVX double-precision exponentiation
• Performance Much faster Pooling for various dimensions
• Feature: Sub matrices in 2D, 3D and 4D
• Feature Helpers for Machine Learning
• Feature Comparisons operators and functions equal, not_equal, almost_equal
• Feature Logical operators for boolean containers
• Feature Shuffle and noise can now operate on custom random engines
• Feature Pooling with stride is now supported
• Feature Custom fast and dyn matrices support
• Feature Matrices and vectors slices view
• Feature Deeper pooling support
• Feature bias_add (2D and 4D) (Machine Learning)
• Feature bias_batch_mean (2D and 4D) (Machine Learning)
• Feature Transposed convolution
• GPU Better usage of contexts
• GPU Pooling and Upsample support
• GPU batch_outer support
• GPU sigmoid and RELU and derivatives
• GPU Memory pool handling
• GPU Avoid a lot of temporaries
• Misc Reduced duplications in the code base
• Misc Simplifications of the iterators to DMA expressions
• Misc Faster compilation of the test cases
• Misc Generalized SSE/AVX versions into VEC versions
• Misc Reviewed completely temporary expressions
• Bug Lots of small fixes
• Bug Transpose on GPU was not working on column major matrix
• Bug 4D Pooling
• Bug Q/R Decomposition

ETL Version 1.0

Initial version (was rolling released before) with the following main features:

• Smart Expression Templates
• Matrix and vector (runtime-sized and compile-time-sized)
• Simple element-wise operations
• Reductions (sum, mean, max, ...)
• Unary operations (sigmoid, log, exp, abs, ...)
• Matrix multiplication
• Convolution (1D and 2D and higher variations)
• Max Pooling
• Fast Fourrier Transform
• Use of SSE/AVX to speed up operations
• Use of BLAS/MKL/CUBLAS/CUFFT/CUDNN libraries to speed up operations
• Symmetric matrix adapter (experimental)
• Sparse matrix (experimental)