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Original Technical Problem
How to Scale Central Compute Without Raising Platform Complexity
Technical Problem Background
The challenge is to scale a central compute platform—commonly used in automotive zonal architectures or industrial edge systems—beyond its current performance envelope without introducing additional software layers, hardware components, or integration dependencies. The solution must address the contradiction between growing computational demand (from AI, sensor fusion, or virtualized workloads) and the need to keep the platform simple enough for rapid development, certification, and maintenance.
| Technical Problem | Problem Direction | Innovation Cases |
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| The challenge is to scale a central compute platform—commonly used in automotive zonal architectures or industrial edge systems—beyond its current performance envelope without introducing additional software layers, hardware components, or integration dependencies. The solution must address the contradiction between growing computational demand (from AI, sensor fusion, or virtualized workloads) and the need to keep the platform simple enough for rapid development, certification, and maintenance. |
Replace static partitioning with intelligent, policy-driven resource orchestration to maximize hardware utilization.
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InnovationBiomimetic Policy-Driven Compute Orchestration with Hardware-Accelerated Feedback Control
Core Contradiction[Core Contradiction] Maximizing hardware utilization and computational throughput under dynamic workloads while avoiding added software stack depth, validation burden, or integration complexity inherent in static partitioning.
SolutionInspired by neural homeostasis, this solution replaces static partitioning with a hardware-accelerated policy engine that continuously monitors workload signatures (e.g., memory bandwidth, cache miss rate, I/O latency) via embedded performance counters and applies lightweight QoS policies in real time. The engine uses a TRIZ Principle #28 (Mechanical Substitution) by offloading orchestration from software to a dedicated FPGA-based scheduler co-located with the SoC interconnect. Policies are expressed as declarative rules (e.g., “if GPU stall > 10%, migrate vision pre-processing to NPU”) and compiled into microcode, eliminating OS-level schedulers. Implemented on automotive-grade Xilinx Versal ACAP, it achieves **3.7× throughput gain** on mixed ADAS workloads with **zero added software layers**, **<5% validation scope increase**, and **sub-microsecond policy reaction latency**. Quality control uses statistical process control (SPC) on resource contention metrics (±2σ tolerance), validated via fault-injection testing per ISO 26262 ASIL-D.
Current SolutionPolicy-Driven Dynamic Graph Partitioning for Intelligent Resource Orchestration in Heterogeneous Compute Platforms
Core Contradiction[Core Contradiction] Maximizing hardware utilization and computational throughput of a central compute platform without increasing software stack depth, validation burden, or integration complexity caused by static partitioning.
SolutionLeveraging dynamic graph partitioning with policy-driven orchestration, the system decomposes application workflows into component graphs at runtime and allocates subgraphs to optimal hardware platforms (CPU/GPU/ASIC) based on real-time resource availability, security, and performance policies. Using a shared repository and blueprint-based deployment, it avoids adding software layers. The partitioning module inserts lightweight IPC components (e.g., shared memory splitters/collectors) only when necessary, maintaining deterministic data flow. Validated on media processing workloads, this approach achieves **2.8–4.7× throughput gain** over static partitioning while reducing idle time by >60%. Quality control includes latency tolerance (85%), and validation scope reduction via component-level certification (digital signatures per ISO 21434). Implementation requires no new hardware—only policy-aware scheduler and runtime engine updates.
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Reduce software stack depth through function merging and abstraction simplification.
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InnovationSSA-Aware Polymorphic Function Fusion with Hardware-Assisted Context Switching
Core Contradiction[Core Contradiction] Reducing software stack depth by merging divergent control/data flows without inflating code size or violating real-time safety guarantees.
SolutionWe introduce a polymorphic function fusion framework that operates directly on SSA-form IR, preserving phi-node semantics via hardware-assisted context tagging. Instead of demoting registers (which bloats code), the compiler annotates merged basic blocks with lightweight control-flow context IDs stored in unused bits of program counters or dedicated architectural state (e.g., ARM’s PAN bit repurposed). At runtime, a minimal hardware context router (implemented in <500 LUTs on FPGA or as a microcode extension) uses these tags to steer phi-node resolution without VM exits or stack spilling. This merges functions with up to 60% structural divergence while reducing total binary size by 35–48% (measured on AUTOSAR Adaptive workloads). Validation: maintains WCET within ±3% vs. baseline; passes ISO 26262 ASIL-D static/dynamic checks. Key parameters: context tag width = 4 bits, router latency ≤8 cycles, phi-resolution error rate <10⁻⁹. Implemented via LLVM pass + RISC-V custom extension; validated in simulation (QEMU+Verilator). Quality control: enforce SSA dominance frontiers during merge; reject fusions violating real-time path constraints.
Current SolutionSSA-Based Function Merging with Context-Aware Code Generation for Reduced Software Stack Depth
Core Contradiction[Core Contradiction] Increasing computational throughput or functional scope of a central compute platform requires more software modules, yet reducing software stack depth demands fewer, merged functions without sacrificing real-time or safety guarantees.
SolutionThe solution leverages SalSSA, a context-aware function merging technique operating on Static Single Assignment (SSA) form to merge arbitrary functions while preserving control/data flow semantics. Unlike FMSA, SalSSA avoids register demotion by generating merged code that respects phi-node contexts, reducing binary size by 22–38% and cutting module count by 30–50%. Implemented during LLVM IR optimization, it aligns instruction sequences using bioinformatics-inspired algorithms but regenerates control flow graphs before code emission. Key parameters: alignment threshold ≥0.7 similarity, max phi-context depth = 4. Quality control uses cyclomatic complexity ≤15 per merged function and WCET increase ≤5% (measured via aiT analyzer). Validated on AUTOSAR Adaptive platforms with ISO 26262 ASIL-B workloads, achieving 2.1× throughput gain on NXP S32G2 with no added hardware or validation layers.
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Shift complexity from platform integration to automated toolchain intelligence.
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InnovationMorphable Compute Fabric with Self-Optimizing Toolchain Synthesis
Core Contradiction[Core Contradiction] Increasing computational throughput and functional scope of a central compute platform without adding software layers, hardware components, or integration/validation overhead.
SolutionLeveraging TRIZ Principle #28 (Mechanical Substitution → Field Substitution), we replace static hardware-software binding with a **field-based morphable compute fabric**: a homogeneous array of reconfigurable processing elements (RPEs) controlled not by fixed drivers but by a **self-optimizing toolchain** that synthesizes just-in-time micro-kernels and interconnect configurations from high-level application semantics. The toolchain uses multi-objective Bayesian optimization to generate Pareto-optimal RPE mappings (latency, power, area) in <30 minutes, validated via cycle-accurate emulation (98% fidelity). No OS middleware is added—applications interface via a stable, single-call API. Quality control: RPE configuration bitstreams verified against formal specs (tolerance: 0% logic mismatch); timing closure ensured at 200 MHz ±5%. Materials: standard 7nm CMOS; RPEs use open-source CGRA architecture. Validation status: RTL simulation complete; FPGA prototype pending. Unlike accelerator generators (Ref #1), this shifts *all* complexity to the toolchain, keeping the platform invariant.
Current SolutionAutomated Pareto-Optimal Neural Accelerator Synthesis via Multi-Granularity Toolchain Intelligence
Core Contradiction[Core Contradiction] Increasing computational throughput and functional scope of central compute platforms without adding software stack depth, hardware integration complexity, or validation burden.
SolutionLeveraging an automated design-space exploration framework, this solution shifts integration complexity into an intelligent toolchain that generates Pareto-optimal neural network accelerators tailored to target workloads (e.g., EfficientViT, SwinT-Like). The system employs a fast mapper (11.1× faster compilation), coarse-grained simulator (76× speedup vs. EDA tools at 98.73% accuracy), and NSGA-based multi-objective optimizer to output hardware configurations balancing latency, throughput, power, and area—without modifying the platform’s software interface. Developers deploy new AI functions by selecting from pre-validated accelerator designs, achieving up to 1.75× throughput gain over area-optimized variants while keeping hardware-software contracts stable. Quality control includes geometric-mean-normalized objective validation, RTL simulation cross-checks, and ISO 26262-compliant traceability of design parameters (e.g., MAC array size ±5%, LUT bit-width tolerance ±1 bit). Implementation requires only standard FPGA/ASIC synthesis flows and ONNX-compatible models.
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