Maximizing resource utilization of neural network computing system
The present disclosure relates to a method for allocating resources of an accelerator to two or more neural networks for execution. The two or more neural networks may include a first neural network and a second neural network. The method comprises analyzing workloads of the first neural network and the second neural network, wherein the first neural network and second neural network each includes multiple computational layers, evaluating computational resources of the accelerator for executing each computational layer of the first and second neural networks, and scheduling computational resources of the accelerator to execute one computational layer of the multiple computation layers of the first neural network and to execute one or more computational layers of the multiple computational layers of the second neural network.
1. A method comprising:
analyzing workloads of a first neural network and a second neural network, wherein the first neural network and second neural network each includes multiple computational layers;
evaluating computational resources of an accelerator for executing each computational layer of the first and second neural networks; and
scheduling computational resources of the accelerator to execute one computational layer of the multiple computation layers of the first neural network and to execute one or more computational layers of the multiple computational layers of the second neural network, wherein scheduling computational resources of the accelerator is performed when a total amount of computational resources for executing the first and second neural networks is bigger than a total amount of computational resources of the accelerator.
2. The method of claim 1 , wherein the first neural network has a first pipeline interval,
wherein an execution time for the one computational layer of the first neural network is shorter than the first pipeline interval, and
wherein scheduling computational resources comprises: scheduling the computational resources of the accelerator to execute the one or more computational layers of the second neural network during a time period corresponding to a difference between the first pipeline interval and the execution time.
3. The method of claim 1 , wherein scheduling computational resources comprises: scheduling the computational resources of the accelerator to execute the one or more computational layers of the second neural network before executing the one computational layer of the first neural network.
4. The method of claim 1 , wherein evaluating computation resources of the accelerator further comprises: comparing the total amount of computational resources for executing the first and second neural networks with the total amount of available computational resources of the accelerator.
5. The method of claim 1 , further comprising:
determining a time period that the computational resources assigned for executing the first neural network are not used during execution of the first neural network,
wherein the one or more computational layers of the second neural network are executed within the time period.
6. The method of claim 1 , wherein the first neural network has a longer pipeline interval than the second neural network.
7. The method of claim 1 , wherein the computational resources of the accelerator are scheduled to execute the one computational layer of the first neural network and the one or more computational layers of the second neural network before executing another computational layer subsequent to the one computation layer of the first neural network.
8. An apparatus comprising:
a memory storing a set of instructions; and
one or more processors configured to execute the set of instructions to cause the apparatus to perform:
analyzing workloads of a first neural network and a second neural network, wherein the first neural network and second neural network each includes multiple computational layers;
evaluating computational resources of an accelerator for executing each computational layer of the first and second neural networks; and
scheduling computational resources of the accelerator to execute one computational layer of the multiple computation layers of the first neural network and to execute one or more computational layers of the multiple computational layers of the second neural network, wherein scheduling computational resources of the accelerator is performed when a total amount of computational resources for executing the first and second neural networks is bigger than a total amount of computational resources of the accelerator.
9. The apparatus of claim 8 , wherein the first neural network has a first pipeline interval,
wherein an execution time for the one computational layer of the first neural network is shorter than the first pipeline interval, and
wherein scheduling computational resources comprises: scheduling the computational resources of the accelerator to execute the one or more computational layers of the second neural network during a time period corresponding to a difference between the first pipeline interval and the execution time.
10. The apparatus of claim 8 , wherein scheduling computational resources comprises: scheduling the computational resources of the accelerator to execute the one or more computational layers of the second neural network before executing the one computational layer of the first neural network.
11. The apparatus of claim 8 , wherein evaluating computation resources of the accelerator further comprises: comparing the total amount of computational resources for executing the first and second neural networks with the total amount of available computational resources of the accelerator.
12. The apparatus of claim 8 , wherein the one or more processors are configured to execute the set of instructions to cause the apparatus to further perform:
determining a time period that the computational resources assigned for executing the first neural network are not used during execution of the first neural network,
wherein the one or more computational layers of the second neural network are executed within the time period.
13. The apparatus of claim 8 , wherein the first neural network has a longer pipeline interval than the second neural network.
14. A non-transitory computer readable medium that stores a set of instructions that is executable by at least one processor of a computing device to cause the computing device to perform a method comprising:
analyzing workloads of a first neural network and a second neural network, wherein the first neural network and second neural network each includes multiple computational layers;
evaluating computational resources of an accelerator for executing each computational layer of the first and second neural networks; and
scheduling computational resources of the accelerator to execute one computational layer of the multiple computation layers of the first neural network and to execute one or more computational layers of the multiple computational layers of the second neural network, wherein scheduling computational resources of the accelerator is performed when a total amount of computational resources for executing the first and second neural networks is bigger than a total amount of computational resources of the accelerator.
15. The computer readable medium of claim 14 , wherein the first neural network has a first pipeline interval,
wherein an execution time for the one computational layer of the first neural network is shorter than the first pipeline interval, and
wherein scheduling computational resources comprises: scheduling the computational resources of the accelerator to execute the one or more computational layers of the second neural network during a time period corresponding to a difference between the first pipeline interval and the execution time.
16. The computer readable medium of claim 14 , wherein scheduling computational resources comprises: scheduling the computational resources of the accelerator to execute the one or more computational layers of the second neural network before executing the one computational layer of the first neural network.
17. The computer readable medium of claim 14 , wherein evaluating computation resources of the accelerator further comprises: comparing the total amount of computational resources for executing the first and second neural networks with the total amount of available computational resources of the accelerator.
18. The computer readable medium of claim 14 , wherein the set of instructions that is executable by at least one processor of the computing device to cause the computing device to further perform:
determining a time period that the computational resources assigned for executing the first neural network are not used during execution of the first neural network,
wherein the one or more computational layers of the second neural network are executed within the time period.
19. The computer readable medium of claim 14 , wherein the first neural network has a longer pipeline interval than the second neural network.