VMULACC

VMulAcc is a vector multiply‑accumulate instruction that is common in many modern vector processing units, including those found in high‑performance computing, graphics processors, and AI accelerators. The instruction multiplies two vectors element‑by‑element and then adds the results into a destination vector in a single, fused operation. Because it combines the multiplication and addition steps, the instruction can produce a more numerically accurate result than performing a separate multiply followed by an add, and it also reduces data movement and instruction overhead.

In typical assembly syntax, a VMulAcc instruction might be written as: VMULACC Vd, Vs, Vt, where Vd is the destination vector, and Vs and Vt are the source vectors. The operation is performed on all active lanes of the vector register in parallel, exploiting the SIMD (Single Instruction, Multiple Data) capability of the processor. Some architectures provide variants that allow saturation, rounding control, or conditional execution, enabling the instruction to be tailored for specific workloads such as integer arithmetic or low‑precision floating‑point.

The instruction is frequently used in inner‐loop kernels for matrix multiplication, convolution, and other linear‑algebra routines. In machine‑learning inference engines, VMulAcc functions as the core of the dot‑product calculation in fully‑connected layers and convolutional kernels. By fusing multiply and accumulate, implementations can reduce power consumption and increase throughput while maintaining high precision.