Here is a list of all topics with brief descriptions:

[detail level 1234]

▼Accumulator Data Types
FP Complex Accumulator Types
Floating-Point Accumulator Types
►Integer Accumulator Types
1024-bit accumulator types
2048-bit accumulator types
256-bit accumulator types
512-bit accumulator types
Integer Complex Accumulator Types
Basic Types: Summary	Summary of the vector and accumulator registers
▼Compiler optimizations	Description of the optimizations automatically performed by the compiler
►Code annotations	Description of the optimizations performed by the compiler based on the annotations provided by the user
Loop annotations	These annotations are used after the heading of the for or while loop. These are meant to customize the behavior of software pipelining
Loop transformations	These annotations are used after heading of the for or while loop
Optimization annotations	These annotations are used to guide the compiler on how to optimize the use of variables
Resource allocation annotations	These annotations are used to interact with the register allocator
Scheduling annotations	These annotations are used to interact with the scheduler
►Code optimizations	Description of the optimizations performed by the compiler to simplify the code
Chain simplification
Chain simplification (different accumulators)
Extract and concat
Extract and concat all elements
Insert and extract generating new vector
Merge of Multiply Accumulate operations
Remove extract and set for same accumulator component
Set-extract
Set-update-extract
Shuffles
Simplification between complex and real accumulators
Skip intermediate extractions
Skip intermediate extractions for 2k accumulators
Undefined simplifications
Conversion Functions	Set of functions for converting types
▼Load/Store Operations
Addressing intrinsics
Load 4x Operations
►Streams
Cascade read
Cascade write
Stream read
Stream write
Load/store to FIFO registers
▼Nonlinear functions
Scalar nonlinear functions
Vector nonlinear functions
Scalar Data Types	All the standard C scalar data-types are supported
▼Scalar Operations
►Configuration
►Mode Settings
Control registers	Intrinsics to set, get and clear the control registers
Status registers	Intrinsics to set,get and clear the status registers
Core ID
Cycle Counter
Events
Initialization
Integer Operations	Scalar division step intrinsic
Locks
Scalar updates and extracts
Stream access	These functions setup stream accesses in native mode
▼Vector Conversions	Various forms of conversions between vector data-types
Brain float (BF16) to integer conversions	Conversion from bfloat16 vector to integer vector
►Broadcast	Broadcasts input value to all vector lanes
Broadcast from scalar	Broadcasts input value to all vector lanes (alternative syntax to broadcast to vector)
Broadcast to vector	Broadcasts input value to all vector lanes (alternative syntax to broadcast from scalar)
Updating all elements with element extracted from vector	Extracts element "idx" from vector "v" and broadcasts its value to all lanes of the destination vector
Updating all elements with one	Broadcasts value one (1) to all vector lanes
Updating all elements with zero	Broadcasts value zero (0) to all vector lanes
Casting	Casting intrinsics allow casting (bit-reinterpretation) between vector types of the same size
►Concatenate vectors	Vector concat intrinsic functions allow concatenation of vector values to create a larger one
Concatenate four vectors
Concatenate two vectors
►Extract vector	Extraction intrinsics enable lanes to be selected from vector and accumulator types
Extract MX vector components
Extract sparse vector components
Extract/insert element	These intrinsics allow inserting or extracting of an individual element into/from a vector
►Insert vector	Vector insert intrinsic functions allow substitution of the lanes within a vector value
Insert MX vector components
Insert a vector into a vector
Insert sparse vector components
Pack/Unpack
►Set vector	Vector set intrinsic functions allow setting the lanes within a vector value
Set MX vector components
Set sparse vector components
►Shift-Round-Saturate	Intrinsics for moving values from accumulator data-types to vector data-types
AIE interface
Floating-point interface
Size interface
►Upshift	Intrinsics for moving values from vector data-types to accumulator data-types
AIE interface
Floating-point
Size interface
▼Vector Data Types
►Brain Floating-Point Vector Types
16-bit Brain Floating-Point Vector Types
8-bit Brain Floating-Point Vector Types
Complex 16-bit Brain Floating-Point Vector Types
Complex Floating-Point Vector Types
Complex Vector Types
►Floating-Point Vector Types
16-bit Floating-Point Vector Types
32-bit Floating-Point Vector Types
8-bit Floating-Point Vector Types
►Integer Vector Types
1024-bit vector types
128-bit vector types
16-bit vector types
256-bit vector types
32-bit vector types
512-bit vector types
64-bit vector types
8-bit vector types
MX Vector Types	Microscaling Formats (MX) vector data-types are stored in the vector registers within the AIE-ML v2
►Sparse Vector Types
Sparse bfloat16/float16 vector types
Sparse float8 vector types
Sparse integer vector types
▼Vector Operations
Add/Subtract	Intrinsics and operators that allows you to perform addition and substraction operations on all types of vectors
Bitwise logical	Intrinsics and operators that allows you to perform bitwise logical operations on all types of vectors
Compare/Select
Initialization
►Multiply Accumulate	Intrinsics allowing you to perform MUL/MAC operations and a few of their variants
Emulated Multiply-accumulate of 16-bit Complex Brain Floating-Point	Elementwise matrix multiplications emulated on top of bfloat16
Emulated Multiply-accumulate of 16b x 32b datatypes	Matrix multiplications in which matrix A has data elements of 16 bit and matrix B has data elements of 32 bit. These operations are emulated on top of Multiply-accumulate of 16b x 16b integer datatypes and might not have optimal performance
Emulated Multiply-accumulate of 32-bit Floating-Point	Elementwise matrix multiplications emulated on top of bfloat16
Emulated Multiply-accumulate of 32b x 16b datatypes	Matrix multiplications in which matrix A has data elements of 32 bit and matrix B has data elements of 16 bit. These operations are emulated on top of Multiply-accumulate of 16b x 16b integer datatypes and might not have optimal performance
Emulated Multiply-accumulate of 32b x 32b datatypes	Matrix multiplications in which matrix A has data elements of 32 bit and matrix B has data elements of 32 bit. These operations are emulated on top of Multiply-accumulate of 32b x 16b integer datatypes and Multiply-accumulate of 16b x 16b integer datatypes and might not have optimal performance
Emulated Multiply-accumulate of Complex 32b x Complex 32b datatypes	Matrix multiplications in which matrix A has data elements of complex 32 bit and matrix B has data elements of complex 32 bit. These operations are emulated on top of Multiply-accumulate of 32b x 16b complex integer datatypes and might not have optimal performance
Emulated Multiply-accumulate of Complex Float and Float datatypes	Elementwise operations based on the already emulated FP32 operations (see Emulated Multiply-accumulate of 32-bit Floating-Point). These operations might not have optimal performance
Multiply-accumulate of 16b x 16b complex integer datatypes
Multiply-accumulate of 16b x 16b integer datatypes
Multiply-accumulate of 32b x 16b complex integer datatypes
Multiply-accumulate of 32b x 16b integer datatypes
Multiply-accumulate of 8b x 8b integer datatypes
Multiply-accumulate of MX4 datatypes
Multiply-accumulate of MX6 datatypes
Multiply-accumulate of MX9 datatypes
Multiply-accumulate of bfloat16 * float16 datatypes
Multiply-accumulate of bfloat16 datatypes
Multiply-accumulate of float16 * bfloat16 datatypes
Multiply-accumulate of float16 datatypes
Multiply-accumulate of float8 and bfloat8 datatypes
Multiply-accumulate with a sparse matrix
Negation control in complex multiplication modes	In order to do complex multiplications, some terms need to be negated
Vector x scalar multiply-accumulate of 16b x 16b complex integer datatypes
Vector x scalar multiply-accumulate of 16b x 16b datatypes
Vector x scalar multiply-accumulate of 32b x 16b complex integer datatypes
Vector x scalar multiply-accumulate of 8b x 8b datatypes
Vector x scalar multiply-accumulate of float16 and bfloat16 datatypes
Shift	These intrinsics allow shifting full vectors
Shift element	These intrinsics allow inserting of an element into a vector by pushing in a new value at one end of the vector
►Shuffle	Intrinsics allowing you perform vector shuffles
Illustration of Shuffle Modes