VTA Configuration

VTA Configuration#

The VTA stack incorporates both a hardware accelerator stack and a TVM based software stack. VTA incorporates flexibility out of the box: by modifying the 3rdparty/vta-hw/config/vta_config.json high-level configuration file, the user can change the shape of the tensor intrinsic, clock frequency, pipelining, data type width, and on-chip buffer sizes.

Parameters Overview#

We explain the parameters listed in the vta_config.json file in the table below.

Attribute

Format

Description

TARGET

String

The TVM device target.

HW_VER

String

VTA hardware version number.

LOG_INP_WIDTH

Int (log2)

Input data type signed integer width.

LOG_WGT_WIDTH

Int (log2)

Weight data type signed integer width.

LOG_ACC_WIDTH

Int (log2)

Accumulator data type signed integer width.

LOG_BATCH

Int (log2)

VTA matrix multiply intrinsic input/output dimension 0.

LOG_BLOCK

Int (log2)

VTA matrix multiply inner dimensions.

LOG_UOP_BUFF_SIZE

Int (log2)

Micro-op on-chip buffer in Bytes.

LOG_INP_BUFF_SIZE

Int (log2)

Input on-chip buffer in Bytes.

LOG_WGT_BUFF_SIZE

Int (log2)

Weight on-chip buffer in Bytes.

LOG_ACC_BUFF_SIZE

Int (log2)

Accumulator on-chip buffer in Bytes.

备注

When a parameter name is preceded with LOG, it means that it describes a value that can only be expressed a power of two. For that reason we describe these parameters by their log2 value. For instance, to describe an integer width of 8-bits for the input data types, we set the LOG_INP_WIDTH to be 3, which is the log2 of 8. Similarly, to descibe a 64kB micro-op buffer, we would set LOG_UOP_BUFF_SIZE to be 16.

We provide additional detail below regarding each parameter:

  • TARGET: Can be set to "pynq", "ultra96", "sim" (fast simulator), or "tsim" (cycle accurate sim with verilator).

  • HW_VER: Hardware version which increments every time the VTA hardware design changes. This parameter is used to uniquely identity hardware bitstreams.

  • LOG_BATCH: Equivalent to A in multiplication of shape (A, B) x (B, C), or typically, the batch dimension of inner tensor computation.

  • LOG_BLOCK: Equivalent to B and C in multiplication of shape (A, B) x (B, C), or typically, the input/output channel dimensions of the inner tensor computation.