Abstract: In addition to the usual modes of SIMD processor operation, where corresponding elements of two source vector registers are used as input pairs to be operated upon by the execution unit, or where one element of a source vector register is broadcast for use across the elements of another source vector register, the new system provides several other modes of operation for the elements of one or two source vector registers. Improving upon the time-costly moving of elements for an operation such as DCT, the present invention defines a more general set of modes of vector operations. In one embodiment, these new modes of operation use a third vector register to define how each element of one or both source vector registers are mapped, in order to pair these mapped elements as inputs to a vector execution unit.

Abstract: The present invention provides an efficient method to implement nested if-then-else conditional statements in a SIMD processor, which requires only one vector compare instruction for both if and else parts of the conditional construct. No stack and stack-handling instructions are needed for vector condition codes. Two condition code flag bits representing if and else parts of testing per element provide for nesting of multiple if-then-else. All SIMD instructions are conditional including the vector compare instruction, and this provides a method for aggregating multiple conditions in nested if-then-else statements. M full levels of if-then-else nesting requires (2M?1) nodes or vector test instructions and 2M+1 condition code flags per vector element. Also, capability to compare any element of first source vector register with any element of second source vector is provided.

Abstract: The present invention performs video and audio compression/decompression, video input and output scaling, video input and output processing for enhancement, and system layer functions on a single semiconductor chip. The media processor is compromised of video processor with a SIMD vector engine, audio processor, stream processor, system processor, and video scalers, LUTs and hardware blender. Unified memory architecture is used where these four processors use a shared memory for data and instructions. Data transfers between multiple processors use multiple packet-based unidirectional communication channels via hardware-assisted circular queues in unified memory. The video processor is a SIMD processor coupled to a regular RISC processor as a dual-issue processor. Such integrated and programmable functionality provides implementation of multiple video and audio for compression standards and programmable video enhancement.

Abstract: The present invention provides histogram calculation for images and video applications using a SIMD and VLIW processor with vector Look-Up Table (LUT) operations. This provides a speed up of histogram calculation by a factor of N times over a scalar processor where the SIMD processor could perform N LUT operations per instruction. Histogram operation is partitioned into a vector LUT operation, followed by vector increment, vector LUT update, and at the end by reduction of vector histogram components. The present invention could be used for intensity, RGBA, YUV, and other type of multi-component images.

Abstract: A media processor for performing audio, video, and system layer tasks on a fully programmable single-chip integrated processor chip. The present invention performs video compression/decompression, audio compression/decompression, video input and output scaling, video input and output processing for enhancement, and system layer functions. The media processor is a single IC semiconductor chip that is coupled to one or more memory chips, audio front-end chip, and optional I/O interface chips. The media processor is compromised of the following integrated on a single IC semiconductor: Video processor with a SIMD vector engine, audio processor, stream processor, system (host) processor, and video scalers, LUTs and hardware blender.

Abstract: The present invention provides histogram calculation for images and video applications using a SIMD and VLIW processor with vector Look-Up Table (LUT) operations. This provides a speed up of histogram calculation by a factor of N times over a scalar processor where the SIMD processor could perform N LUT operations per instruction. Histogram operation is partitioned into a vector LUT operation, followed by vector increment, vector LUT update, and at the end by reduction of vector histogram components. The present invention could be used for intensity, RGBA, YUV, and other type of multi-component images.

Abstract: The apparatus provides for efficient implementation of multiple-bit leap-forward LFSR calculation in a SIMD processor. This provides an accelerated and programmable way to implement LFSR calculations in a SIMD processor. Conditional vector exclusive-OR accumulation is used by manipulating the leap-forward matrix, whereby one conditional vector exclusive-OR operation is performed for each column and partial results are accumulated. For an N-wide SIMD this results in close to N times acceleration of leap-forward LFSR calculation without additional resources or dedicated logic.

Abstract: The apparatus provides for efficient implementation of multiple-bit leap-forward LFSRu calculation in a SIMD processor. This provides an accelerated and programmable way to implement LFSR calculations in a SAID processor. Conditional vector exclusive-OR accumulation is used by manipulating the leap-forward matrix, whereby one conditional vector exclusive-OR operation is performed for each column and partial results are accumulated. For an N-wide SIMD this results in close to N times acceleration of leap-forward LFSR calculation without additional resources or dedicated logic.

Abstract: The present invention provides a 16×16-sliding window using vector register file with zero overhead for horizontal or vertical shifts to incorporate motion estimation into SIMD vector processor architecture. SIMD processor's vector load mechanism, vector register file with shifting of elements capability, and 16×16 parallel SAD calculation hardware and instruction are used. Vertical shifts of all sixteen-vector registers occur in a ripple-through fashion when the end vector register is loaded. The parallel SAD calculation hardware can calculate one 16-by-16-block match per clock cycle in a pipelined fashion. In addition, hardware for best-match SAD value comparisons and maintaining their pixel location reduces the software overhead. Block matching for less than 16 by 16 block areas is supported using a mask register to mask selected elements, thereby reducing search area to any block size less than 16 by 16.

Abstract: The present invention provides efficient ways to implement scan conversion and matrix transpose operations using vector multiplex operations in a SIMD processor. The present method provides a very fast and flexible way to implement different scan conversions, such as zigzag conversion, and matrix transpose for 2×2, 4×4, 8×8 blocks commonly used by all video compression and decompression algorithms.