Vector Data Types - 2023.2 English

AI Engine Kernel and Graph Programming Guide (UG1079)

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2023.2 English

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


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 128b/256b/512b/1024b vector is supported, with 512b being the default.

Table 1. Supported Vector Types and Sizes
Vector Types Sizes 1
int8 16/32/64/128
int16 8/16/32/64
int32 4/8/16/32
uint8 16/32/64/128
float 4/8/16/32
cint16 4/8/16/32
cint32 2/4/8/16
cfloat 2/4/8/16
  1. The integer in bold is the native vector size for the data type supported in the AI Engine. For example, aie::broadcast((cfloat){1,1}) is equivalent to aie::broadcast<cfloat,8>((cfloat){1,1}), where specifying <cfloat,8> is optional because it is the native vector size for the cfloat data type.

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.


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.

Table 2. Supported Accumulator Types and Sizes
Accumulator Type acc32 cacc32 acc40 cacc40 acc48 cacc48 acc56 cacc56 acc64 cacc64 acc72 cacc72 acc80 cacc80 accfloat caccfloat
Native accumulation bits 48 80 32