system-dse-deepstack

# System DSE with DeepStack Design Space Exploration (DSE) methodology for system-hardware co-design using DeepStack performance modeling. ## Problem Statement Cross-stack co-design is increasingly critical for AI efficiency. Traditional DSE approaches are either: - Too slow for early-stage exploration - Too inaccurate for reliable decisions - Don't account for distributed system effects ## Solution: DeepStack Accurate and efficient performance model for early-stage system-hardware co-design space exploration for distributed 3D-stacked AI systems. ## Core Methodology ### Step 1: Define Design Space ```python Design parameters: - System topology (distributed nodes) - 3D-stack configuration (memory stacking) - AI workload characteristics - Interconnect bandwidth/latency - Compute unit allocation ``` ### Step 2: Performance Modeling DeepStack models performance across layers: ```markdown Stack layers: 1. Application layer (AI workload) 2. System layer (distributed topology) 3. Hardware layer (3D-stacked memory + logic) 4. Interconnect layer (chiplet communication) ``` ### Step 3: Design Space Exploration ```python DSE process: 1. Define constraints (power, area, latency) 2. Generate candidate designs 3. Evaluate with DeepStack model 4. Filter by performance criteria 5. Select Pareto-optimal designs ``` ### Step 4: Early-stage Decision Making ```markdown Use DeepStack for: - Architecture selection - Memory hierarchy design - Distribution strategy - Hardware-software partitioning ``` ## Key Techniques ### Cross-stack Performance Modeling ```python # Performance model components: compute_latency = model_compute(workload, hardware) memory_bandwidth = model_memory(3d_stack_config) interconnect_cost = model_interconnect(topology) total_performance = aggregate_stack(compute, memory, interconnect) ``` ### Scalable DSE ```markdown Optimization strategies: - Prune infeasible designs early - Use surrogate models for fast evaluation - Parallel exploration of design branches - Incremental refinement of promising designs ``` ### Accuracy vs Efficiency Tradeoff ```python # Balance accuracy and speed: early_stage: fast surrogate models mid_stage: hybrid (surrogate + detailed) final_stage: detailed simulation ``` ## Workflow Example **Scenario**: Designing distributed AI accelerator for vision transformer. ```markdown 1. Define workload: Vision Transformer inference 2. Define constraints: 100W power, 10ms latency 3. Generate candidates: - 2-node vs 4-node distribution - 2-stack vs 4-stack memory - Different interconnect bandwidths 4. Evaluate with DeepStack 5. Filter by latency/power constraints 6. Select Pareto-optimal: 4-node, 4-stack, high bandwidth ``` ## Best Practices 1. **Start broad, refine later**: Explore wide design space early 2. **Use constraints early**: Prune infeasible designs 3. **Cross-stack thinking**: Consider all layers together 4. **Validate models**: Compare with real measurements 5. **Document decisions**: Track why designs were selected/rejected ## Applications - AI accelerator design - Chiplet-based system design - 3D-stacked memory optimization - Distributed inference systems - System-on-chip architecture selection ## Design Parameters Reference | Parameter | Range | Impact | |-----------|-------|--------| | Node count | 2-16 | Parallelism, communication cost | | Stack height | 2-8 layers | Memory bandwidth, thermal | | Interconnect BW | 10-100 GB/s | Distribution efficiency | | Compute units | 4-64 | Throughput, power | ## Performance Metrics ```markdown Key metrics: - Latency (ms) - Throughput (samples/s) - Power (W) - Energy efficiency (samples/J) - Area (mm²) - Cost ($) ``` ## Related Work - **DeepFlow**: Cross-stack pathfinding framework - **Timeloop**: DSE for accelerators - **Cosmic**: Chiplet-based design - **Accelergy**: Energy estimation ## Source Paper **DeepStack: Scalable and Accurate Design Space Exploration for Distributed 3D-stacked AI Systems** - arxiv ID: 2604.04750 - Published: April 2026 ## Related Skills - **kg-research-workflow**: Import papers to knowledge graph - **arxiv-search**: Search for latest systems papers - **skill-creator**: Create skills from research ## Notes - Early-stage DSE saves significant design time - Accuracy critical for reliable decisions - Cross-stack modeling essential for AI systems - 3D-stacking introduces thermal constraints - Distributed topology affects communication cost