Vitis 2023.2

Documentation changes

• Integrate AIE-ML documentation
• Document rounding modes
• Expand accumulate documentation
• Clarify limitations on 8b parallel lookup
• Fix mmul member documentation
• Clarify requirement for linear_approx step bits
• Improve documentation of vector, accum, and mask
• Highlight architecture requirements of functions using C++ requires clauses
• Document FFT twiddle factor generation
• Clarify internal rounding mode for bfloat16 to integer conversion
• Clarify native and emulated modes for mmul
• Clarify native and emulated modes for sliding_mul
• Document sparse_vector_input_buffer_stream with memory layout and GEMM example
• Document tensor_buffer_stream with a GEMM example

Global AIE API changes

• Add cfloat support for AIE-ML

Changes to data types

• vector: Optimize grow_replicate on AIE-ML
• mmul: Support reinitialization from an accum
• DM resources: Add compound aie_dm_resource variants
• streams: Add sparse_vector_input_buffer_stream for loading sparse data on AIE-ML
• streams: Add tensor_buffer_stream to handle multi-dimensional addressing for AIE-ML
• bfloat16: Add specialization for std::numeric_limits on AIE-ML

Changes to operations

• abs: Fix for float input
• add_reduce: Optimize for 8b and 16b types on AIE-ML
• div: Implement vector-vector and vector-scalar division
• downshift: Implement logical_downshift for AIE
• fft: Add support for 32 bit twiddles on AIE
• fft: Fix for radix-3 and radix-5 FFTs on AIE
• fft: Fix radix-5 performance for low vectorizations on AIE
• fft: Add stage-based FFT functions and deprecate iterator interface
• mul: Fix for vector * vector_elem_ref on AIE
• print_fixed: Support printing Q format data
• print_matrix: Added accumulator support
• sliding_mul: Add support float
• sliding_mul: Add support for remaining 32b modes for AIE-ML
• sliding_mul: Add support for Points < Native Points
• sliding_mul_ch: Fix DataStepX == DataStepY requirement
• sincos: Optimize AIE implementation
• to_fixed: Fix for AIE-ML
• to_fixed/to_float: Add vectorized float conversions for AIE
• to_fixed/to_float: Add generic conversions ((int8, int16, int32) <-> (bfloat16, float)) for AIE-ML

ADF integration

• Add TLAST support for stream reads on AIE-ML
• Add support for input_cascade and output_cascade types
• Deprecate accum reads from input_stream and output_stream

Vitis 2023.1

Documentation changes

• Add explanation of FFT inputs
• Use block_size in FFT docs
• Clarify matrix data layout expectations
• Clarify downshift being arithmetic
• Correct description of bfloat16 linear_approx lookup table

Global AIE API changes

• Do not explicitly initialize inferred template arguments
• More aggressive inlining of internal functions
• Avoid using 128b vectors in stream helper functions for AIE-ML

Changes to data types

• iterator: Do not declare iterator data members as const
• mask: Optimized implementation for 64b masks on AIE-ML
• mask: New constructors added to initialize the mask from uint32 or uint64 values
• vector: Fix 1024b inserts
• vector: Use 128b concats in upd_all
• vector: Fix 8b unsigned to_vector for AIE-ML

Changes to operations

• add/sub: Support for dynamic accumulator zeroization
• begin_restrict_vector: Add implementation for io_buffer
• eq: Add support for complex numbers
• fft: Correctly set radix configuration in fft::begin_stage calls
• inv/invsqrt: Add implementation for AIE-ML
• linear_approx: Performance optimization for AIE-ML
• logical_downshift: New function that implements a logical downshift (as opposed to aie::downshift, which is arithmetic)
• max/min/maxdiff: Add support for dynamic sign
• mmul: Implement 16b 8x2x8 mode for AIE-ML
• mmul: Implement 8b 8x8x8 mode for AIE-ML
• mmul: Implemet missing 16b x 8b and 8b x 4b sparse multiplication modes for AIE-ML
• neq: Add support for complex numbers
• parallel_lookup: Optimize implementation for signed truncation
• print_matrix: New function that prints vectors with the specified matrix shape
• shuffle_up/down: Minor optimization for 16b
• shuffle_up/down: Optimized implementation for AIE-ML
• sliding_mul: Support data_start/coeff_start values larger than vector size
• sliding_mul: Add support for 32b modes for AIE-ML
• sliding_mul: Add 2 point 16b 16 channel for AIE-ML
• sliding_mul_ch: New function for multi-channel multiplication modes for AIE-ML
• sliding_mul_sym_uct: Fix for 16b two-buffer implementation
• store_unaligned_v: Optimized implementation for AIE-ML
• transpose: Add support for 64b and 32b types
• transpose: Enable transposition of 256 element 4b vectors (scalar implementation for now)
• to_fixed: Add bfloat16 to int32 conversion on AIE-ML

Vitis 2022.2

Documentation changes

• Add code samples for load_v/store_v and load_unaligned_v/store_unaligned_v
• Enhanced documentation for parallel_lookup and linear_approx
• Clarify coeff vector size limit on AIE-ML

Global AIE API changes

• Remove usage of srs in compare functions, to avoid compilation warnings as it is deprecated
• Add support for stream ADF vector types on AIE-ML

Changes to data types

• mask: add shift operators
• saturation_mode: add saturate value. It was previously named truncate, which is not correct. The old name is also kept until it is deprecated

Changes to operations

• add: support accumulator addition on AIE-ML
• add_reduce: add optimized implementation for cfloat on AIE
• add_reduce: add optimized implementation for bfloat16 on AIE-ML
• eq/neq: enhanced implementation on AIE-ML
• le: enhanced implementation on AIE-ML
• load_unaligned_v: leverage pointer truncation to 128b done by HW on AIE
• fft: add support for radix 3/5 on AIE
• mmul: add matrix x vector multiplicatio modes on AIE
• mmul: add support for dynamic accumulator zeroization
• to_fixed: added implementation for AIE-ML
• to_fixed: provide a default return type
• to_float: added implementation for AIE-ML
• reverse: optimized implementation for 32b and 64b on AIE-ML
• zeros: include fixes on AIE

Vitis 2022.1

Documentation changes

• Small documentation fixes for operators
• Issues of documentation on msc_square and mmul
• Enhance documentation for sliding_mul operations
• Change logo in documentation
• Add documentation for ADF stream operators

Global AIE API changes

• Add support for emulated FP32 data types and operations on AIE-ML

Changes to data types

• unaligned_vector_iterator: add new type and helper functions
• random_circular_vector_iterator: add new type and helper functions
• iterator: add linear iterator type and helper functions for scalar values
• accum: add support for dynamic sign in to/from_vector on AIE-ML
• accum: add implicit conversion to float on AIE-ML
• vector: add support for dynamic sign in pack/unpack
• vector: optimization of initialization by value on AIE-ML
• vector: add constructor from 1024b native types on AIE-ML
• vector: fixes and optimizations for unaligned_load/store

Changes to operations

• adf::buffer_port: add many wrapper iterators
• adf::stream: annotate read/write functions with stream resource so they can be scheduled in parallel
• adf::stream: add stream operator overloading
• fft: performance fixes on AIE-ML
• max/min/maxdiff: add support for bfloat16 and float on AIE-ML
• mul/mmul: add support for bfloat16 and float on AIE-ML
• mul/mmul: add support for dynamic sign AIE-ML
• parallel_lookup: expanded to int16->bfloat, performance optimisations, and softmax kernel
• print: add support to print accumulators
• add/max/min_reduce: add support for float on AIE-ML
• reverse: add optimized implementation on AIE-ML using matrix multiplications
• shuffle_down_replicate: add new function
• sliding_mul: add 32b for 8b * 8b and 16b * 16b on AIE-ML
• transpose: add new function and implementation for AIE-ML
• upshift/downshift: add implementation for AIE-ML

Vitis 2021.2

Documentation changes

• Fix description of sliding_mul_sym_uct
• Make return types explicit for better documentation
• Fix documentation for sin/cos so that it says that the input must be in radians
• Add support for concepts
• Add documenttion for missing arguments and fix wrong argument names
• Fixes in documentation for int4/uint4 AIE-ML types
• Add documentation for the mmul class
• Update documentation about supported accumulator sizes
• Update the matrix multiplication example to use the new MxKxN scheme and size_A/size_B/size_C

Global AIE API changes

• Make all entry points always_inline
• Add declaration macros to aie_declaration.hpp so that they can be used in headers parsed by aiecompiler

Changes to data types

• Add support for bfloat16 data type on AIE-ML
• Add support for cint16/cint32 data types on AIE-ML
• Add an argument to vector::grow, to specify where the input vector will be located in the output vector
• Remove copy constructor so that the vector type becomes trivial
• Remove copy constructor so that the mask type becomes trivial
• Make all member functions in circular_index constexpr
• Add tiled_mdspan::begin_vector_dim functions that return vector iterators
• Initial support for sparse vectors on AIE-ML, including iterators to read from memory
• Make vector methods always_inline
• Make vector::push be applied to the object it is called on and return a reference

Changes to operations

• add: Implementation optimization on AIE-ML
• add_reduce: Implement on AIE-ML
• bit/or/xor: Implement scalar x vector variants of bit operations
• equal/not_equal: Add fix in which not all lanes were being compared for certain vector sizes.
• fft: Interface change to enhance portability across AIE/AIE-ML
• fft: Add initial support on AIE-ML
• fft: Add alignment checks for x86sim in FFT iterators
• fft: Make FFT output interface uniform for radix 2 cint16 upscale version on AIE
• filter_even/filter_odd: Functional fixes
• filter_even/filter_odd: Performance improvement for 4b/8b/16b implementations
• filter_even/filter_odd: Performance optimization on AIE-ML
• filter_even/filter_odd: Do not require step argument to be a compile-time constant
• interleave_zip/interleave_unzip: Improve performance when configuration is a run-time value
• interleave_*: Do not require step argument to be a compile-time constant
• load_floor_v/load_floor_bytes_v: New functions that floor the pointer to a requested boundary before performing the load.
• load_unaligned_v/store_unaligned_v: Performance optimization on AIE-ML
• lut/parallel_lookup/linear_approx: First implementation of look-up based linear functions on AIE-ML.
• max_reduce/min_reduce: Add 8b implementation
• max_reduce/min_reduce: Implement on AIE-ML
• mmul: Implement new shapes for AIE-ML
• mmul: Initial support for 4b multiplication
• mmul: Add support for 80b accumulation for 16b x 32b / 32b x 16b cases
• mmul: Change dimension names from MxNxK to MxKxN
• mmul: Add size_A/size_B/size_C data members
• mul: Optimized mul+conj operations to merged into a single intrinsic call on AIE-ML
• sin/cos/sincos: Fix to avoid int -> unsigned conversions that reduce the range
• sin/cos/sincos: Use a compile-time division to compute 1/PI
• sin/cos/sincos: Fix floating-point range
• sin/cos/sincos: Optimized implementation for float vector
• shuffle_up/shuffle_down: Elements don't wrap around anymore. Instead, new elements are undefined.
• shuffle_up_rotate/shuffle_down_rotate: New variants added for the cases in which elements need to wrap-around
• shuffle_up_replicate: Variant added which replicates the first element.
• shuffle_up_fill: Variant added which fills new elements with elements from another vector.
• shuffle_*: Optimization in shuffle primitives on AIE, especially for 8b/16b cases
• sliding_mul: Fixes to handle larger Step values for cfloat variants
• sliding_mul: Initial implementation for 16b x 16b and cint16b x cint16b on AIE-ML
• sliding_mul: Optimized mul+conj operations to merged into a single intrinsic call on AIE-ML
• sliding_mul_sym: Fixes in start computation for filters with DataStepX > 1
• sliding_mul_sym: Add missing int32 x int16 / int16 x int32 type combinations
• sliding_mul_sym: Fix two-buffer sliding_mul_sym acc80
• sliding_mul_sym: Add support for separate left/right start arguments
• store_v: Support pointers annotated with storage attributes