IP Library Granted Patent US 12,093,531
Granted Patent B2
US 12,093,531 · App. 17/507,616 · Granted Sep 17, 2024

Hardware accelerator

Inventors: Wei-Chun Chang (Taoyuan, TW); Yuan-Hsiang Kuo (Hsinchu, TW); Chia-Lin Lu (Hsinchu, TW); Hsueh-Chien Lu (Hsinchu, TW)
Assignee: Cvitek Co. Ltd.
G06F3/0613G06F3/0659G06F3/0673G06F12/0207G06F17/16G06T1/60G06T3/4046G06T2207/20084G06V10/82
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,093,531
App. No.
17/507,616
Granted
Sep 17, 2024
Kind
B2
Abstract

A hardware accelerator is provided. The hardware accelerator includes a first memory; a source address generation unit coupled to the first memory; a data collection unit coupled to the first memory; a first data queue coupled to the data collection unit; a data dispersion unit coupled to the first data queue; a destination address generation unit coupled to the data dispersion unit; an address queue coupled to the destination address generation unit; a second data queue coupled to the data dispersion unit; and a second memory coupled to the second data queue. The hardware accelerator can perform anyone or any combination of tensor stride, tensor reshape and tensor transpose to achieve tensorflow depth-to-space permutation or tensorflow space-to-depth permutation.

Claims (20)

1. A hardware accelerator, comprising:

a first memory used for receiving data;

a source address generation unit coupled to the first memory for generating a plurality of source addresses to the first memory according to a source shape parameter and a source stride parameter, wherein the first memory transmits data according to the source addresses;

a data collection unit coupled to the first memory used for receiving data transmitted from the first memory;

a first data queue coupled to the data collection unit used for temporarily storing data transmitted from the data collection unit;

a data dispersion unit coupled to the first data queue used for dispersing data transmitted from the first data queue;

a destination address generation unit coupled to the data dispersion unit for generating a plurality of destination addresses according to a destination shape parameter and a destination stride parameter;

an address queue coupled to the destination address generation unit for temporarily storing the destination addresses generated by the destination address generation unit;

a second data queue coupled to the data dispersion unit for temporarily storing data transmitted from the data dispersion unit; and

a second memory coupled to the second data queue for writing data transmitted from the second data queue according to the destination addresses generated by the destination address generation unit,

wherein the hardware accelerator performs tensor stride, tensor reshape and tensor transpose to achieve tensorflow depth-to-space permutation and tensorflow space-to-depth permutation, and

after tensor stride is performed, the source shape parameter and the destination shape parameter are the same, but the source stride parameter and the destination stride parameter are different.

2. The hardware accelerator according to claim 1 , wherein the source shape parameter and the source stride parameter are user defined parameters.

3. The hardware accelerator according to claim 1 , wherein the destination shape parameter and the destination stride parameter are user defined parameters.

4. The hardware accelerator according to claim 1 , wherein after tensor reshape is performed, the source shape parameter and the destination shape parameter are different.

5. The hardware accelerator according to claim 1 , wherein after tensor transpose is performed, the source shape parameter and the destination shape parameter are different.

6. The hardware accelerator according to claim 1 , wherein tensorflow depth-to-space permutation is achieved by performing tensor transpose, tensor reshape and tensor stride, and tensor reshape and tensor stride.

7. The hardware accelerator according to claim 1 , wherein tensorflow depth-to-space permutation is achieved through a plurality of repeats of tensor stride.

8. The hardware accelerator according to claim 1 , wherein tensorflow space-to-depth permutation is achieved by performing tensor reshape and tensor stride, and tensor reshape and tensor transpose.

9. The hardware accelerator according to claim 1 , wherein tensorflow space-to-depth permutation is achieved through a plurality of repeats of tensor stride.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jul 10, 2025
From: CVITEK CO. LTD.
To: SHENZHEN SUANHAI TECHNOLOGY CO., LTD.
Reel/Frame 071894/0052 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 25, 2021
From: LU, HSUEH-CHIEN
To: CVITEK CO. LTD.
Reel/Frame 057898/0884 →
Priority Claims (1)
CN 202011196269.3 · Oct 30, 2020 · national
Continuity (1)
Related Publication 20220137826A1 · May 5, 2022