Phase 3: The Hardware Co-Designer
Moving on to Phase 3: The Hardware Co-Designer is the crucial step that validates the core product claim: that your Agentic AI can automate the joint optimization of both software and custom hardware.
This phase requires the Agent to reason about physical components—cache sizes, memory bandwidth, accelerator parameters—and design a hardware blueprint that is perfectly tailored to the optimized software pipeline from Phase 2.
🚀 User Story and Acceptance Criteria for Milestone 3.1
Section titled “🚀 User Story and Acceptance Criteria for Milestone 3.1”The Hardware Co-Designer (Custom Silicon Architecture)
Section titled “The Hardware Co-Designer (Custom Silicon Architecture)”Milestone 3.1 focuses on proving the Agent’s ability to automate the hardware design process, resulting in maximal energy efficiency for the specific drone application.
👤 User Story
Section titled “👤 User Story”As a Custom Silicon Engineer,
I want the Agent to automatically generate a validated hardware configuration (e.g., FPGA parameters or ASIC micro-architecture parameters) based on the energy profile of the optimized software,
So that I can skip manual Design Space Exploration and proceed directly to synthesizing a power-optimized custom accelerator and memory subsystem.
✅ Acceptance Criteria (A/C)
Section titled “✅ Acceptance Criteria (A/C)”These criteria ensure the Agent is truly performing co-design by modifying and verifying the virtual hardware itself.
| ID | Criterion Description | Key Metrics (Must be Reported) | Engineering Focus |
|---|---|---|---|
| A/C 3.1.1 | Architectural Generation | The Agent must autonomously modify and validate at least two independent hardware parameters (e.g., L2 Cache Size and NPU/DSP Array Dimension) during a single DSE run. The output must be a parsable configuration file. | Integration: Successful linkage between the Agent’s reasoning core and the hardware simulator’s configuration inputs (e.g., gem5 config files or HLS parameters). |
| A/C 3.1.2 | Co-Design Efficiency Uplift | The final Co-Designed HW/SW configuration must demonstrate an additional $30%$ improvement in $\text{Performance} / \text{Watt}$ compared to the optimized software from Milestone 2.2 running on the baseline, off-the-shelf hardware. | Value Proposition: Proving that joint optimization (HW and SW) delivers a greater benefit than optimizing software alone. |
| A/C 3.1.3 | Constraint-Aware Design | Given a strict power budget constraint (e.g., “Max $8\text{W}$ TDP”), the Agent must output a valid hardware configuration that, according to the Power Estimator, does not exceed $8\text{W}$, even if a higher-performance configuration was found. | Thermal Management: Validating the Agent’s ability to trade performance for power based on critical operational constraints (passive cooling). |
| A/C 3.1.4 | Hardware Bottleneck Resolution | The Agent must successfully identify the primary hardware bottleneck (e.g., memory bandwidth saturation) and resolve it by proposing a hardware change (e.g., doubling the bus width or optimizing the data layout) that reduces the bottleneck utilization by $40%$. | Deep Reasoning: Demonstrating the Agent’s ability to analyze low-level micro-architectural data and translate it into effective design changes. |
🏁 User Story and Acceptance Criteria for Milestone 3.2
Section titled “🏁 User Story and Acceptance Criteria for Milestone 3.2”The Sim-to-Real Golden Design (MVP Launch)
Section titled “The Sim-to-Real Golden Design (MVP Launch)”Milestone 3.2 is the final step for the MVP, where the Agent’s theoretical design is validated in the real world, confirming the accuracy of the entire co-simulation pipeline.
👤 User Story
Section titled “👤 User Story”As a Product Manager,
I want the Agent’s finalized HW/SW blueprint package to be deployed and validated on a physical drone testbed,
So that I can confidently claim and market the predicted energy efficiency gains and flight time improvements to our potential customers.
✅ Acceptance Criteria (A/C)
Section titled “✅ Acceptance Criteria (A/C)”These criteria focus on the successful transfer of the design from the simulation to a physical system and the verification of the key claim: energy efficiency.
| ID | Criterion Description | Key Metrics (Must be Reported) | Engineering Focus |
|---|---|---|---|
| A/C 3.2.1 | Deployment Success | The Agent’s outputted software binary and hardware configuration (loaded onto an FPGA or similar programmable platform) must successfully boot and complete the specified baseline mission scenario on a physical drone testbed. | Toolchain Validation: Ensuring the Agent-generated configurations are compatible with physical synthesis tools and the drone’s flight stack. |
| A/C 3.2.2 | Sim-to-Real Accuracy (Energy) | The actual measured flight time and power consumption of the optimized design on the physical drone must be within $10%$ of the value predicted by the Co-Simulation Environment. | Calibration: The critical test of the entire platform’s validity. High accuracy confirms the Digital Twin is reliable. |
| A/C 3.2.3 | Efficiency Proof | The physical drone, running the Agent’s Co-Designed HW/SW configuration, must achieve a minimum $50%$ increase in flight time (or range) compared to the original, unoptimized baseline drone setup. | Product Validation: The ultimate proof of concept for the energy efficiency claim to secure funding/sales. |
| A/C 3.2.4 | Blueprint Package Finalization | The Agent must deliver a final, version-controlled Blueprint Package that includes the optimized binary, the final hardware configuration files, the Bill of Materials (BoM), and a full simulation-to-real-world validation report. | Go-to-Market Readiness: Creating a deployable, auditable product artifact for customer handover. |