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It is just a representation of AND which does its work on the bits of the operands rather than the truth value of the operands. Bitwise binary AND performs logical conjunction (shown in the table above) of the bits in each position of a number in its binary form. For instance, working with a byte (the char type):
MLIR (Multi-Level Intermediate Representation) is a unifying software framework for compiler development. [1] MLIR can make optimal use of a variety of computing platforms such as central processing units (CPUs), graphics processing units (GPUs), data processing units (DPUs), Tensor Processing Units (TPUs), field-programmable gate arrays (FPGAs), artificial intelligence (AI) application ...
In compiler design, static single assignment form (often abbreviated as SSA form or simply SSA) is a type of intermediate representation (IR) where each variable is assigned exactly once. SSA is used in most high-quality optimizing compilers for imperative languages, including LLVM , the GNU Compiler Collection , and many commercial compilers.
A further development within LLVM is the use of Multi-Level Intermediate Representation with the potential to generate code for different heterogeneous targets, and to combine the outputs of different compilers. [6] The ILOC intermediate language [7] is used in classes on compiler design as a simple target language. [8]
SQUOZE was a compressed binary form of assembly language code and included a symbol table. Modern IBM mainframe operating systems, such as z/OS, have available a symbol table named Associated data (ADATA). The table is stored in a file that can be produced by the IBM High-Level Assembler (HLASM), [20] IBM's COBOL compiler, [21] and IBM's PL/I ...
A bitwise AND is a binary operation that takes two equal-length binary representations and performs the logical AND operation on each pair of the corresponding bits. Thus, if both bits in the compared position are 1, the bit in the resulting binary representation is 1 (1 × 1 = 1); otherwise, the result is 0 (1 × 0 = 0 and 0 × 0 = 0).
A fixed-point representation of a fractional number is essentially an integer that is to be implicitly multiplied by a fixed scaling factor. For example, the value 1.23 can be stored in a variable as the integer value 1230 with implicit scaling factor of 1/1000 (meaning that the last 3 decimal digits are implicitly assumed to be a decimal fraction), and the value 1 230 000 can be represented ...
The half-precision binary floating-point exponent is encoded using an offset-binary representation, with the zero offset being 15; also known as exponent bias in the IEEE 754 standard. [9] E min = 00001 2 − 01111 2 = −14; E max = 11110 2 − 01111 2 = 15; Exponent bias = 01111 2 = 15