## Bar Chart: Normalized Performance Across Model Configurations and Architectures ### Overview The image is a grouped bar chart comparing normalized performance metrics across six hardware/software configurations (BF16-Q4, FP16-Q4, BF16-Q8, FP16-Q8, FP32-Q4, FP32-Q8) and seven model sizes (OPT-125M to OPT-30B). Performance is measured on a normalized scale (0–3), with five distinct hardware accelerators represented by color-coded bars (FPE, FIGLUT-F, iFPU, FIGNA, FIGLUT-I). ### Components/Axes - **X-Axis**: Divided into six main categories (BF16-Q4, FP16-Q4, BF16-Q8, FP16-Q8, FP32-Q4, FP32-Q8), each containing seven subcategories for model sizes (OPT-125M, OPT-350M, OPT-1.3B, OPT-2.7B, OPT-6.7B, OPT-13B, OPT-30B). - **Y-Axis**: Labeled "Normalized Performance" with a scale from 0 to 3. - **Legend**: Located at the bottom-right, mapping colors to accelerators: - Gray: FPE - Pink: FIGLUT-F - Green: iFPU - Blue: FIGNA - Orange: FIGLUT-I ### Detailed Analysis #### BF16-Q4 Section - **OPT-125M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.14 (pink) - iFPU: 1.31 (green) - FIGNA: 1.92 (blue) - FIGLUT-I: 2.84 (orange) - **OPT-350M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.14 (pink) - iFPU: 1.31 (green) - FIGNA: 1.92 (blue) - FIGLUT-I: 2.84 (orange) - **OPT-1.3B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.14 (pink) - iFPU: 1.30 (green) - FIGNA: 1.92 (blue) - FIGLUT-I: 2.82 (orange) - **OPT-2.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.14 (pink) - iFPU: 1.30 (green) - FIGNA: 1.92 (blue) - FIGLUT-I: 2.82 (orange) - **OPT-6.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.14 (pink) - iFPU: 1.30 (green) - FIGNA: 1.92 (blue) - FIGLUT-I: 2.82 (orange) - **OPT-13B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.14 (pink) - iFPU: 1.30 (green) - FIGNA: 1.92 (blue) - FIGLUT-I: 2.82 (orange) - **OPT-30B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.14 (pink) - iFPU: 1.30 (green) - FIGNA: 1.92 (blue) - FIGLUT-I: 2.82 (orange) #### FP16-Q4 Section - **OPT-125M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.17 (pink) - iFPU: 1.48 (green) - FIGNA: 2.17 (blue) - FIGLUT-I: 2.95 (orange) - **OPT-350M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.17 (pink) - iFPU: 1.48 (green) - FIGNA: 2.17 (blue) - FIGLUT-I: 2.95 (orange) - **OPT-1.3B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.17 (pink) - iFPU: 1.47 (green) - FIGNA: 2.17 (blue) - FIGLUT-I: 2.95 (orange) - **OPT-2.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.17 (pink) - iFPU: 1.47 (green) - FIGNA: 2.17 (blue) - FIGLUT-I: 2.95 (orange) - **OPT-6.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.17 (pink) - iFPU: 1.47 (green) - FIGNA: 2.17 (blue) - FIGLUT-I: 2.95 (orange) - **OPT-13B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.17 (pink) - iFPU: 1.47 (green) - FIGNA: 2.17 (blue) - FIGLUT-I: 2.94 (orange) - **OPT-30B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.17 (pink) - iFPU: 1.47 (green) - FIGNA: 2.17 (blue) - FIGLUT-I: 2.94 (orange) #### BF16-Q8 Section - **OPT-125M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.70 (green) - FIGNA: 1.46 (blue) - FIGLUT-I: 1.73 (orange) - **OPT-350M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.70 (green) - FIGNA: 1.46 (blue) - FIGLUT-I: 1.73 (orange) - **OPT-1.3B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.69 (green) - FIGNA: 1.46 (blue) - FIGLUT-I: 1.71 (orange) - **OPT-2.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.69 (green) - FIGNA: 1.46 (blue) - FIGLUT-I: 1.71 (orange) - **OPT-6.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.69 (green) - FIGNA: 1.46 (blue) - FIGLUT-I: 1.71 (orange) - **OPT-13B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.69 (green) - FIGNA: 1.46 (blue) - FIGLUT-I: 1.71 (orange) - **OPT-30B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.69 (green) - FIGNA: 1.46 (blue) - FIGLUT-I: 1.71 (orange) #### FP16-Q8 Section - **OPT-125M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.67 (green) - FIGNA: 1.54 (blue) - FIGLUT-I: 1.69 (orange) - **OPT-350M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.67 (green) - FIGNA: 1.54 (blue) - FIGLUT-I: 1.69 (orange) - **OPT-1.3B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.66 (green) - FIGNA: 1.54 (blue) - FIGLUT-I: 1.68 (orange) - **OPT-2.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.66 (green) - FIGNA: 1.54 (blue) - FIGLUT-I: 1.68 (orange) - **OPT-6.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.66 (green) - FIGNA: 1.54 (blue) - FIGLUT-I: 1.68 (orange) - **OPT-13B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.66 (green) - FIGNA: 1.54 (blue) - FIGLUT-I: 1.68 (orange) - **OPT-30B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.66 (green) - FIGNA: 1.54 (blue) - FIGLUT-I: 1.68 (orange) #### FP32-Q4 Section - **OPT-125M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.28 (pink) - iFPU: 1.33 (green) - FIGNA: 3.07 (blue) - FIGLUT-I: 3.24 (orange) - **OPT-350M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.28 (pink) - iFPU: 1.33 (green) - FIGNA: 3.07 (blue) - FIGLUT-I: 3.24 (orange) - **OPT-1.3B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.27 (pink) - iFPU: 1.33 (green) - FIGNA: 3.07 (blue) - FIGLUT-I: 3.24 (orange) - **OPT-2.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.27 (pink) - iFPU: 1.33 (green) - FIGNA: 3.07 (blue) - FIGLUT-I: 3.24 (orange) - **OPT-6.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.27 (pink) - iFPU: 1.33 (green) - FIGNA: 3.07 (blue) - FIGLUT-I: 3.24 (orange) - **OPT-13B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.27 (pink) - iFPU: 1.33 (green) - FIGNA: 3.07 (blue) - FIGLUT-I: 3.24 (orange) - **OPT-30B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.27 (pink) - iFPU: 1.33 (green) - FIGNA: 3.07 (blue) - FIGLUT-I: 3.24 (orange) #### FP32-Q8 Section - **OPT-125M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.76 (green) - FIGNA: 2.51 (blue) - FIGLUT-I: 2.91 (orange) - **OPT-350M**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.76 (green) - FIGNA: 2.51 (blue) - FIGLUT-I: 2.91 (orange) - **OPT-1.3B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.76 (green) - FIGNA: 2.51 (blue) - FIGLUT-I: 2.91 (orange) - **OPT-2.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.76 (green) - FIGNA: 2.51 (blue) - FIGLUT-I: 2.91 (orange) - **OPT-6.7B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.76 (green) - FIGNA: 2.51 (blue) - FIGLUT-I: 2.91 (orange) - **OPT-13B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.76 (green) - FIGNA: 2.51 (blue) - FIGLUT-I: 2.91 (orange) - **OPT-30B**: - FPE: 1.00 (gray) - FIGLUT-F: 1.00 (pink) - iFPU: 0.76 (green) - FIGNA: 2.51 (blue) - FIGLUT-I: 2.91 (orange) ### Key Observations 1. **Performance Trends**: - **Larger Models**: Generally show higher performance in FP32-Q4/Q8 configurations (e.g., OPT-30B reaches 3.24 in FP32-Q4). - **Hardware Accelerators**: - **FIGLUT-I** consistently outperforms others across most configurations (e.g., 2.84–3.24 in BF16/FP32). - **FPE** (gray) is the baseline, often at 1.00, suggesting it is the reference point. - **iFPU** (green) shows moderate gains in FP32 configurations but underperforms in BF16/FP16. - **Quantization Impact**: BF16/FP16 configurations show lower performance than FP32, especially for smaller models (e.g., OPT-125M drops from 3.24 in FP32-Q4 to 1.92 in BF16-Q4). 2. **Anomalies**: - **iFPU Underperformance**: In BF16/FP16 configurations, iFPU (green) lags significantly behind other accelerators (e.g., 0.70–1.48 vs. 1.92–2.95 for others). - **FIGLUT-F Consistency**: Maintains near-identical values (1.14–1.28) across configurations, suggesting limited sensitivity to quantization. ### Interpretation The data highlights the interplay between model size, quantization (BF16/FP16/FP32), and hardware acceleration. Key insights include: - **Quantization Trade-offs**: FP32 configurations maximize performance for larger models (e.g., OPT-30B at 3.24), while BF16/FP16 configurations reduce performance but may offer efficiency benefits. - **Hardware Acceleration**: FIGLUT-I (orange) is the most effective accelerator, particularly in FP32, while FPE (gray) serves as a baseline. iFPU (green) underperforms in lower-precision settings, indicating potential architectural mismatches. - **Model Size Scaling**: Larger models (e.g., OPT-30B) benefit more from FP32 and FIGLUT-I, suggesting diminishing returns for smaller models in high-precision settings. This analysis underscores the importance of aligning hardware acceleration strategies with model size and precision requirements to optimize performance.