## Line Graph: Generalized Soft Bounds Device Model ### Overview A line graph showing weight fluctuations across four devices (Dev1–Dev4) over 2100 pulses. The y-axis ranges from -1 to 2, with overlapping lines indicating device-specific weight variations. ### Components/Axes - **X-axis**: Pulse Number (0–2100) - **Y-axis**: Weight (-1 to 2) - **Legend**: - Dev1: Dark blue diamonds - Dev2: Teal diamonds - Dev3: Green diamonds - Dev4: Yellow diamonds ### Detailed Analysis - **Dev1 (Dark Blue)**: Starts at ~1.5, dips to ~0.5 at pulse 800, then rises to ~1.8 by pulse 2100. - **Dev2 (Teal)**: Peaks at ~1.8 near pulse 0, drops to ~0.2 at pulse 800, then stabilizes at ~1.6. - **Dev3 (Green)**: Begins at ~1.2, fluctuates between ~0.8 and ~1.5, ending at ~1.4. - **Dev4 (Yellow)**: Starts at ~0.9, dips to ~0.3 at pulse 800, then rises to ~1.3. ### Key Observations - All devices exhibit periodic dips around pulse 800. - Dev1 and Dev2 show the most pronounced fluctuations. - Dev4 has the smallest amplitude in weight changes. ### Interpretation The graph suggests device-specific weight dynamics under a generalized model. The synchronized dip at pulse 800 may indicate a shared response to a stimulus or system reset. Dev1’s final weight (~1.8) implies higher sensitivity compared to others. --- ## Bar Chart: 3FC MNIST Training ### Overview A bar chart comparing test accuracy (%) of five training methods over 80 epochs. The y-axis ranges from 90% to 100%. ### Components/Axes - **X-axis**: Epochs (0–80, labeled "a.u.") - **Y-axis**: Test Accuracy (%) - **Legend**: - CMO/HFOₓ exp. array: Yellow diamonds - NSR down to 20%: Red circles - Nstates up to 100: Blue squares - Symmetry (SP_skew 50%): Orange crosses - SGD: Green pluses - FP-baseline: Green pluses (baseline) ### Detailed Analysis - **CMO/HFOₓ exp. array**: Peaks at ~98% by 80 epochs. - **NSR down to 20%**: Reaches ~97% by 80 epochs. - **Nstates up to 100**: Stabilizes at ~96.5%. - **Symmetry (SP_skew 50%)**: ~97.5% at 80 epochs. - **SGD**: ~94% at 80 epochs. - **FP-baseline**: ~98% (constant across epochs). ### Key Observations - CMO/HFOₓ exp. array and FP-baseline achieve the highest accuracy. - Symmetry (SP_skew 50%) outperforms Nstates and NSR. - SGD lags significantly behind other methods. ### Interpretation CMO/HFOₓ exp. array and FP-baseline demonstrate superior convergence, suggesting robust training dynamics. The FP-baseline’s consistency implies it serves as a strong benchmark. SGD’s lower performance highlights its limitations in this context. --- ## Diagram: LSTM Network Trained Using CMO/HFOₓ Statistical Array Data ### Overview A block diagram of an LSTM network processing input tokens ("The", "man", "walks", etc.) to output "street". ### Components 1. **Input Layer**: - Tokens converted to one-hot encoding (87xN). 2. **LSTM Layers**: - **LSTM1**: 64 hidden units, 87x64xN input. - **LSTM2**: 64 hidden units, 64x64xN input. 3. **Fully Connected (FC) Layer**: 64x87x1 output. 4. **Output**: "street" (87x1). ### Flow Input → One-Hot Encoding → LSTM1 → LSTM2 → FC → Output. ### Key Observations - The network uses two LSTM layers for sequential processing. - The FC layer maps hidden states to output tokens. ### Interpretation This architecture is designed for sequence-to-sequence tasks, leveraging LSTM’s memory capabilities. The one-hot encoding ensures discrete token representation, while the FC layer finalizes predictions. --- ## Line Graph: LSTM Training ### Overview A line graph comparing test perplexity (1–5) of three methods over 100 epochs. ### Components/Axes - **X-axis**: Epochs (0–100, labeled "a.u.") - **Y-axis**: Test Perplexity - **Legend**: - AGAD: Yellow diamonds - CMO/HFOₓ exp. array: Orange crosses - FP-baseline: Green pluses ### Detailed Analysis - **AGAD**: Starts at ~3.5, decreases to ~2.0 by 100 epochs. - **CMO/HFOₓ exp. array**: Drops from ~3.0 to ~2.2. - **FP-baseline**: Remains flat at ~1.2. ### Key Observations - AGAD shows the steepest decline in perplexity. - FP-baseline maintains the lowest perplexity throughout. ### Interpretation AGAD’s rapid improvement suggests effective training dynamics. FP-baseline’s stability indicates it may represent an optimized or pre-trained model. The divergence between AGAD and FP-baseline highlights differences in training strategies.