# US 2026/0093522 A1 · Runtime monitoring of machine learning-based scheduling algorithms toward robust domain-specific systems-on-chip

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- Number: US 2026/0093522 A1
- Document ID: US20260093522A1
- Status: Published application
- Kind: A1
- Application number: 18/899,711
- Filed: 2024-09-27
- Published: 2026-04-02
- Earliest claimed: 2024-09-27
- Assignees: Wisconsin Alumni Research Foundation
- Applicants: Wisconsin Alumni Research Foundation
- Inventors: Umit Ogras; Ahmet Alper Goksoy; Alish Kanani
- Web page: https://patentgrep.com/patent/US20260093522A1

## CPC

- G06F 9/4881: {Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues}
- G06F 9/4887: {involving deadlines, e.g. rate based, periodic}
- G06F 15/7807: {System on chip, i.e. computer system on a single chip; System in package, i.e. computer system on one or more chips in a single package}
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)

## Abstract

Systems and methods of runtime monitoring a machine learning-based (ML) scheduler for a system-on-chip (SoC) including determining a scheduling action by the ML-based scheduler for an SoC task, the scheduling action based on a policy trained from training data, permitting the scheduling action to be processed by at least one processing element of the SoC, evaluating a quality of the scheduling action, and determining the scheduling action does not generalize to the policy based on the quality of the scheduling action. The policy can be incrementally retrained to generate an updated policy.

## Claims

### Claim 1

A method of runtime monitoring a machine learning-based (ML) scheduler for a system-on-chip (SoC), the method comprising:
determining a scheduling action by the ML-based scheduler for an SoC task, the scheduling action based on a policy trained from training data;
permitting the scheduling action to be processed by at least one processing element of the SoC;
evaluating a quality of the scheduling action; and
determining the scheduling action does not generalize to the policy based on the quality of the scheduling action.

### Claim 2 (depends on 1)

The method of claim 1, wherein the method executes in SoC background not on a critical path of SoC operation.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
incrementally retraining the policy to generate an updated policy; and
implementing the updated policy for the ML-based scheduler.

### Claim 4 (depends on 1)

The method of claim 1, wherein determining the scheduling action does not generalize to the policy based on the quality of the scheduling action further comprises:
calculating a gradient of the policy for a batch of SoC tasks and associated scheduling actions, wherein the policy is grained with gradient descent;
calculating a coherence based on the gradient; and
comparing the coherence against a coherence threshold, wherein a sustained high coherence reflects the scheduling action being not generalized to the policy.

### Claim 5 (depends on 4)

The method of claim 4, further comprising:
triggering incremental retraining of the policy based on the coherence being above the coherence threshold.

### Claim 6 (depends on 1)

The method of claim 1, wherein the ML-based scheduler is implemented by imitation learning, wherein evaluating the quality of the scheduling action further comprises:
implementing a reference scheduler trained by a neural network using at least the training data;
obtaining a ground truth action from the reference scheduler for the SoC task; and
calculating a loss based on the ground truth action and the scheduling action.

### Claim 7 (depends on 6)

The method of claim 6, further comprising:
incrementally retraining the policy to generate an updated policy using the ground truth action and an SoC state pair; and
implementing the updated policy for the ML-based scheduler.

### Claim 8 (depends on 1)

The method of claim 1, wherein the ML-based scheduler is implemented by reinforcement learning, wherein evaluating the quality of the scheduling action further comprises:
implementing a critic network initially trained based on the training data;
obtaining a critic value for the SoC task from the critic network; and
calculating a loss function using the critic value and a reward associated with the scheduling action for the SoC task from the policy implemented by reinforcement learning.

### Claim 9 (depends on 8)

The method of claim 8, further comprising:
incrementally retraining the policy to generate an updated policy using the SoC task and the reward associated with the scheduling action for the SoC task; and
implementing the updated policy for the ML-based scheduler.

### Claim 10 (depends on 1)

The method of claim 1, wherein the scheduling action is evaluated as part of a batch of SoC tasks and associated scheduling actions.

### Claim 11

A system for runtime monitoring for a system-on-chip (SoC), the system comprising:
at least one processing element (PE) configured to execute SoC application tasks;
a memory operably coupled to the at least one PE;
a runtime task scheduler implementing a machine learning (ML)-based policy for SoC application task scheduling; and
a runtime monitor configured to:
determine a scheduling action by the ML-based scheduler for an SoC application task,
permit the scheduling action to be processed by the at least one PE,
evaluate a quality of the scheduling action, and
determine the scheduling action does not generalize to the policy based on the quality of the scheduling action.

### Claim 12 (depends on 11)

The system of claim 11, wherein the runtime monitor executes in SoC background not on a critical path of SoC operation.

### Claim 13 (depends on 11)

The system of claim 11, wherein the runtime monitor is further configured to:
incrementally retrain the policy to generate an updated policy; and
implement the updated policy on the ML-based scheduler.

### Claim 14 (depends on 11)

The system of claim 11, wherein the runtime monitor is further configured to determine the scheduling action does not generalize to the policy based on the quality of the scheduling action including:
calculating a gradient of the policy for a batch of SoC application tasks and associated scheduling actions, wherein the policy is grained with gradient descent;
calculating a coherence based on the gradient; and
comparing the coherence against a coherence threshold, wherein a sustained high coherence reflects the scheduling action being not generalized to the policy.

### Claim 15 (depends on 14)

The system of claim 14, wherein the runtime monitor is further configured to trigger incremental retraining of the policy based on the coherence being above the coherence threshold.

### Claim 16 (depends on 11)

The system of claim 11, wherein the ML-based scheduler is implemented by imitation learning (IL) and the runtime task scheduler is an IL-based monitor, wherein evaluating the quality of the scheduling action further comprises:
implementing a reference scheduler trained by a neural network using at least the training data;
obtaining a ground truth action from the reference scheduler for the SoC application task; and
calculating a loss based on the ground truth action and the scheduling action.

### Claim 17 (depends on 16)

The system of claim 16, wherein the IL-based monitor is further configured to:
incrementally retrain the policy to generate an updated policy using the ground truth action and an SoC state pair; and
implement the updated policy for the ML-based scheduler.

### Claim 18 (depends on 11)

The system of claim 11, wherein the ML-based scheduler is implemented by reinforcement learning (RL) and the runtime task scheduler is an RL-based monitor, wherein evaluating the quality of the scheduling action further comprises:
implementing a critic network initially trained based on the training data;
obtaining a critic value for the SoC task from the critic network; and
calculating a loss function using the critic value and a reward associated with the scheduling action for the SoC task from the policy implemented by reinforcement learning.

### Claim 19 (depends on 18)

The system of claim 18, wherein the RL-based monitor is further configured to:
incrementally retrain the policy to generate an updated policy using the SoC task and the reward associated with the scheduling action for the SoC task; and
implement the updated policy for the ML-based scheduler.

### Claim 20

A computer readable media comprising non-transitory computer executable instructions which, when executed by at least one processing element on a system-on-chip (SoC), perform at least:
determining a scheduling action by the ML-based scheduler for an SoC task, the scheduling action based on a policy trained from training data;
permitting the scheduling action to be processed by at least one processing element of the SoC;
evaluating a quality of the scheduling action; and
determining the scheduling action does not generalize to the policy based on the quality of the scheduling action.