Vector Data Types - 2024.1 English

The two main vector types offered by the AI Engine API are vectors (aie::vector) and accumulators (aie::accum).

Vector

A vector represents a collection of elements of the same type which is transparently mapped to the corresponding vector registers supported on AI Engine architectures. Vectors are parameterized by the element type and the number of elements, and any combination that defines a 128 b/256 b/512 b/1024 b vector is supported, with 512 b being the default.

For example, aie::vector<int32,16> is a 16 element vector of integers with 32 bits. Each element of the vector is referred to as a lane. Using the smallest bit width necessary can improve performance by making good use of registers.

Figure 1. aie::vector<int32,16>

Complex integer and complex float data can be accessed with the member functions real and imag to extract the real and imagery parts of the data. The real part is stored in the low address, and the imaginary part is stored in the high address. For example:

cint8 ctmp={1,2};

// print real and imag values
printf("real=%d imag=%d\n",ctmp.real,ctmp.imag);

// store real and imag values
printf("ctmp mem storage=%llx\n",*(long long*)&ctmp);
cint32 ctmp2={3,4};
int32 *p_ctmp2=reinterpret_cast<int32*>(&ctmp2);

// print "real=3" and "imag=4"
printf("real=%d imag=%d\n",p_ctmp2[0],p_ctmp2[1]);

aie::vector and aie::accum have member functions to do type casting, data extraction and insertion, and indexing. These operations are covered in following sections.

Accumulator

An accumulator represents a collection of elements of the same class, typically obtained as a result of a multiplication operation, which is transparently mapped to the corresponding accumulator registers supported on each architecture. Accumulators commonly provide a large number of bits, allowing users to perform long chains of operations whose intermediate results might exceed the range of regular vector types. Accumulators are parameterized by the element type, and the number of elements. The native accumulation bits define the minimum number of bits and the AI Engine API maps different types to the nearest native accumulator type that supports the requirement. For example, acc40 maps to acc48 for the AI Engine architecture.

The following table lists the accumulator data types that are supported by AI Engine API in kernel coding. For the kernel cascade interface and graph support, see Stream Data Types.