Sync model repo (text/metadata)

README.md

@@ -14,22 +14,22 @@ tags:
   - imagenet-1k
 ---
 
-# DeiT-Tiny optimized for Arm-based Edge NPU
+# DeiT-Tiny optimized for Arm-based Ethos-U NPU
 
-DeiT-Tiny image classification optimized as a quantized ExecuTorch `.pte` model for Arm-based Edge NPU systems (Ethos-U85). Most quantized layers use INT8 activations and INT8 weights (a8w8), while selected output.dense layers use INT16 activations and INT8 weights (a16w8) to preserve accuracy within acceptable ranges. Quantization is per-tensor throughout: weights use symmetric quantization (zero-point fixed at 0) across all layers, while a8w8 activations use an asymmetric scheme (affine, histogram-observer-derived zero-point); the a16w8-overridden output.dense activations are symmetric (zero-point fixed at 0), still histogram-observer-derived.
+DeiT-Tiny image classification optimized as a quantized ExecuTorch `.pte` model for Arm-based Ethos-U NPU systems (Ethos-U85). Most quantized layers use INT8 activations and INT8 weights (a8w8), while selected output.dense layers use INT16 activations and INT8 weights (a16w8) to preserve accuracy within acceptable ranges. Quantization is per-tensor throughout: weights use symmetric quantization (zero-point fixed at 0) across all layers, while a8w8 activations use an asymmetric scheme (affine, histogram-observer-derived zero-point); the a16w8-overridden output.dense activations are symmetric (zero-point fixed at 0), still histogram-observer-derived.
 
 ## Summary
 
-This repository contains an Arm-optimized version of facebook/deit-tiny-patch16-224 for image classification. The model is provided in ExecuTorch `.pte` format, targeting Arm-based Edge NPU systems.
+This repository contains an Arm-optimized version of facebook/deit-tiny-patch16-224 for image classification. The model is provided in ExecuTorch `.pte` format, targeting Arm-based Ethos-U NPU systems.
 
-This version demonstrates efficient inference on Arm-based platforms while preserving the original model's intended behavior. Arm evaluated the model on ImageNet-1k and measured on-device performance on an Alif DK-E8 (Cortex-M55 + Ethos-U85 NPU).
+This version demonstrates efficient inference on Arm-based platforms while preserving the original model's intended behavior. Arm evaluated the model on ImageNet-1k and measured performance on a representative evaluation target.
 
 **Key results**
 
 | Area | Result |
 |---|---|
 | Model format | ExecuTorch `.pte` |
-| Target device class | Edge NPU |
+| Target device class | Ethos-U NPU |
 | Reference device | Alif DK-E8 (Cortex-M55 + Ethos-U85, bare-metal) |
 | Primary performance result | 81.59 ms p50 latency (12.26 inferences/s) |
 | Accuracy result | Top-1 67.95% / Top-5 88.47% (PyTorch quantized simulation, not measured on-device — see Limitations) |