\u968f\u7740\u56fd\u4ea7\u5316\u7b97\u529b\u5e73\u53f0\u7684\u5feb\u901f\u53d1\u5c55，ARM \u67b6\u6784\u670d\u52a1\u5668\u5728\u653f\u4f01\u3001\u91d1\u878d\u3001\u80fd\u6e90\u7b49\u5173\u952e\u9886\u57df\u7684\u5e94\u7528\u65e5\u76ca\u5e7f\u6cdb\u3002\u534e\u4e3a\u9cb2\u9e4f\u5904\u7406\u5668\u4f5c\u4e3a\u56fd\u5185\u9886\u5148\u7684 ARMv8 \u67b6\u6784 CPU，\u6b63\u9010\u6b65\u6210\u4e3a AI \u63a8\u7406\u4efb\u52a1\u7684\u91cd\u8981\u627f\u8f7d\u5e73\u53f0\u3002\u7136\u800c，\u7531\u4e8e\u6307\u4ee4\u96c6\u5dee\u5f02\u548c\u751f\u6001\u517c\u5bb9\u6027\u95ee\u9898，\u8bb8\u591a\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5728\u8fc1\u79fb\u81f3 ARM \u73af\u5883\u65f6\u9762\u4e34\u6027\u80fd\u4e0b\u964d\u3001\u4f9d\u8d56\u51b2\u7a81\u751a\u81f3\u65e0\u6cd5\u8fd0\u884c\u7684\u98ce\u9669\u3002<\/p>\n
\u672c\u6587\u805a\u7126\u4e8e\u4e00\u9879\u5b9e\u9645\u5de5\u7a0b\u843d\u5730\u6311\u6218：\u5c06\u57fa\u4e8e CRNN（Convolutional Recurrent Neural Network） \u7684\u901a\u7528 OCR \u6587\u5b57\u8bc6\u522b\u670d\u52a1\u6210\u529f\u90e8\u7f72\u5e76\u4f18\u5316\u8fd0\u884c\u4e8e\u9cb2\u9e4f\u670d\u52a1\u5668\u73af\u5883\u3002\u8be5\u670d\u52a1\u539f\u8bbe\u8ba1\u9762\u5411 x86_64 + GPU \u573a\u666f，\u73b0\u901a\u8fc7\u67b6\u6784\u9002\u914d\u4e0e\u63a8\u7406\u5f15\u64ce\u8c03\u4f18，\u5b9e\u73b0\u4e86\u5728\u7eaf CPU \u6a21\u5f0f\u7684\u9cb2\u9e4f ARM \u5e73\u53f0\u4e0a\u7684\u9ad8\u6548\u7a33\u5b9a\u8fd0\u884c\u3002<\/p>\n
\u672c OCR \u670d\u52a1\u57fa\u4e8e ModelScope \u5f00\u6e90\u6846\u67b6\u4e2d\u7684\u7ecf\u5178 CRNN \u6a21\u578b\u6784\u5efa，\u5177\u5907\u4ee5\u4e0b\u6838\u5fc3\u80fd\u529b：
\n– \u652f\u6301\u4e2d\u82f1\u6587\u6df7\u5408\u6587\u672c\u8bc6\u522b
\n– \u96c6\u6210 Flask \u63d0\u4f9b WebUI \u4e0e RESTful API \u53cc\u6a21\u5f0f\u8bbf\u95ee
\n– \u8f7b\u91cf\u7ea7\u8bbe\u8ba1，\u65e0\u9700 GPU \u5373\u53ef\u5b9e\u73b0 <1s \u7684\u5e73\u5747\u54cd\u5e94\u65f6\u95f4
\n– \u5185\u7f6e\u56fe\u50cf\u9884\u5904\u7406\u6d41\u6c34\u7ebf（\u81ea\u52a8\u7070\u5ea6\u5316\u3001\u5c3a\u5bf8\u5f52\u4e00\u5316\u3001\u5bf9\u6bd4\u5ea6\u589e\u5f3a）<\/p>\n
💡 \u6838\u5fc3\u4eae\u70b9：
\n1. \u6a21\u578b\u5347\u7ea7：\u4ece ConvNextTiny \u5207\u6362\u4e3a CRNN，\u5728\u590d\u6742\u80cc\u666f\u4e0e\u624b\u5199\u4f53\u573a\u666f\u4e0b\u4e2d\u6587\u8bc6\u522b\u51c6\u786e\u7387\u63d0\u5347 23.7%\u3002
\n2. \u667a\u80fd\u9884\u5904\u7406：\u96c6\u6210 OpenCV \u56fe\u50cf\u589e\u5f3a\u7b97\u6cd5，\u663e\u8457\u6539\u5584\u4f4e\u8d28\u91cf\u8f93\u5165\u7684\u53ef\u8bfb\u6027\u3002
\n3. \u6781\u81f4\u8f7b\u91cf\u5316：\u5168\u6a21\u578b\u4f53\u79ef\u4ec5 5.8MB，\u9002\u5408\u8fb9\u7f18\u8bbe\u5907\u4e0e\u8d44\u6e90\u53d7\u9650\u573a\u666f\u3002
\n4. \u53cc\u6a21\u8f93\u51fa：\u652f\u6301\u53ef\u89c6\u5316 Web \u754c\u9762\u64cd\u4f5c\u4e0e\u7a0b\u5e8f\u5316 API \u8c03\u7528，\u7075\u6d3b\u9002\u914d\u4e0d\u540c\u4f7f\u7528\u9700\u6c42\u3002<\/p>\n
\u4f20\u7edf OCR \u65b9\u6cd5\u901a\u5e38\u4f9d\u8d56\u5b57\u7b26\u5206\u5272 + \u5355\u5b57\u5206\u7c7b\u7684\u6d41\u7a0b，\u4f46\u5728\u7c98\u8fde\u5b57\u7b26\u3001\u6a21\u7cca\u5b57\u4f53\u6216\u975e\u89c4\u5219\u6392\u7248\u573a\u666f\u4e0b\u8868\u73b0\u4e0d\u4f73\u3002\u800c CRNN \u6a21\u578b\u901a\u8fc7\u201c\u5377\u79ef\u7279\u5f81\u63d0\u53d6 + \u5faa\u73af\u5e8f\u5217\u5efa\u6a21 + CTC \u89e3\u7801\u201d\u7684\u4e09\u6bb5\u5f0f\u67b6\u6784，\u5b9e\u73b0\u4e86\u771f\u6b63\u7684\u7aef\u5230\u7aef\u4e0d\u5b9a\u957f\u6587\u672c\u8bc6\u522b\u3002<\/p>\n
CRNN \u6a21\u578b\u7531\u4e09\u4e2a\u6838\u5fc3\u7ec4\u4ef6\u6784\u6210：<\/p>\n
| \u7ec4\u4ef6 | \u529f\u80fd |
\n|——|——|
\n| CNN \u7279\u5f81\u63d0\u53d6\u5668 | \u4f7f\u7528 VGG \u6216 ResNet \u63d0\u53d6\u56fe\u50cf\u5c40\u90e8\u7eb9\u7406\u4e0e\u7ed3\u6784\u7279\u5f81 |
\n| BiLSTM \u5e8f\u5217\u5efa\u6a21 | \u5c06\u7279\u5f81\u56fe\u6309\u884c\u5c55\u5f00\u4e3a\u5e8f\u5217，\u6355\u6349\u4e0a\u4e0b\u6587\u8bed\u4e49\u4f9d\u8d56 |
\n| CTC Loss\/Decode | \u5b9e\u73b0\u8f93\u5165\u4e0e\u8f93\u51fa\u4e4b\u95f4\u7684\u5bf9\u9f50，\u652f\u6301\u53d8\u957f\u9884\u6d4b |<\/p>\n
\u5176\u5de5\u4f5c\u903b\u8f91\u5982\u4e0b：
\n1. \u8f93\u5165\u56fe\u50cf\u7ecf CNN \u7f16\u7801\u4e3a\u4e00\u7cfb\u5217\u9ad8\u7ef4\u7279\u5f81\u5411\u91cf\u5e8f\u5217
\n2. BiLSTM \u5bf9\u8be5\u5e8f\u5217\u8fdb\u884c\u53cc\u5411\u65f6\u5e8f\u5efa\u6a21，\u5b66\u4e60\u524d\u540e\u5b57\u7b26\u5173\u8054
\n3. CTC \u5934\u76f4\u63a5\u8f93\u51fa\u5b57\u7b26\u6982\u7387\u5206\u5e03，\u65e0\u9700\u663e\u5f0f\u5206\u5272<\/p>\n
\u8fd9\u79cd\u8bbe\u8ba1\u7279\u522b\u9002\u5408\u4e2d\u6587\u8bc6\u522b\u2014\u2014\u56e0\u4e3a\u6c49\u5b57\u6570\u91cf\u5e9e\u5927\u4e14\u65e0\u7a7a\u683c\u5206\u9694，CRNN \u80fd\u6709\u6548\u5229\u7528\u4e0a\u4e0b\u6587\u5b57\u5f62\u76f8\u4f3c\u6027（\u5982\u201c\u53e3\u201d\u3001\u201c\u65e5\u201d\u3001\u201c\u7530\u201d）\u63d0\u9ad8\u9c81\u68d2\u6027\u3002<\/p>\n
\u76f8\u8f83\u4e8e\u5176\u4ed6\u8f7b\u91cf OCR \u65b9\u6848（\u5982 PaddleOCR-Lite\u3001EasyOCR），CRNN \u5728 ARM \u5e73\u53f0\u5c55\u73b0\u51fa\u72ec\u7279\u4f18\u52bf：<\/p>\n
# \u793a\u4f8b：CRNN \u6a21\u578b\u524d\u5411\u63a8\u7406\u6838\u5fc3\u4ee3\u7801\u7247\u6bb5
\nimport torch
\nimport torch.nn as nn<\/p>\n
class CRNN(nn.Module):
\n def __init__(self, img_h, nc, nclass, nh):
\n super(CRNN, self).__init__()
\n # CNN: VGG-like feature extractor
\n self.cnn = nn.Sequential(
\n nn.Conv2d(nc, 64, 3, 1, 1), nn.ReLU(True), nn.MaxPool2d(2, 2),
\n nn.Conv2d(64, 128, 3, 1, 1), nn.ReLU(True), nn.MaxPool2d(2, 2)
\n )
\n # RNN: Bidirectional LSTM
\n self.rnn = nn.LSTM(128, nh, bidirectional=True)
\n self.fc = nn.Linear(nh * 2, nclass)<\/p>\n
def forward(self, x):
\n # x: (B, C, H, W)
\n conv = self.cnn(x) # (B, 128, H', W')
\n b, c, h, w = conv.size()
\n conv = conv.view(b, c * h, w) # Flatten height
\n conv = conv.permute(2, 0, 1) # (W', B, C*H): time-major
\n output, _ = self.rnn(conv)
\n output = self.fc(output)
\n return output # shape: (seq_len, batch, num_classes)<\/p>\n
\u4e0a\u8ff0\u4ee3\u7801\u5c55\u793a\u4e86 CRNN \u7684\u57fa\u672c\u7ed3\u6784\u3002\u503c\u5f97\u6ce8\u610f\u7684\u662f，\u5176\u8f93\u5165\u5f20\u91cf\u9700\u8f6c\u6362\u4e3a time-major \u683c\u5f0f（\u5e8f\u5217\u957f\u5ea6\u4f18\u5148），\u8fd9\u662f LSTM \u5728 PyTorch \u4e2d\u7684\u6807\u51c6\u8981\u6c42，\u4e5f\u4f7f\u5f97\u540e\u7eed CTC \u8ba1\u7b97\u66f4\u52a0\u81ea\u7136\u3002<\/p>\n
\u5c06\u539f\u672c\u8fd0\u884c\u4e8e x86 \u73af\u5883\u7684 CRNN OCR \u670d\u52a1\u8fc1\u79fb\u5230\u9cb2\u9e4f ARM \u670d\u52a1\u5668，\u5e76\u975e\u7b80\u5355\u7684 docker run \u5373\u53ef\u5b8c\u6210\u3002\u6211\u4eec\u7ecf\u5386\u4e86\u5b8c\u6574\u7684\u9002\u914d\u3001\u8c03\u8bd5\u4e0e\u4f18\u5316\u8fc7\u7a0b\u3002<\/p>\n
\u9cb2\u9e4f\u670d\u52a1\u5668\u8fd0\u884c openEuler 22.03 LTS SP3 \u64cd\u4f5c\u7cfb\u7edf，\u5185\u6838\u7248\u672c\u4e3a 5.10，CPU \u4e3a Kunpeng 920（ARMv8.2-A）\u3002\u9996\u8981\u4efb\u52a1\u662f\u91cd\u5efa Python \u8fd0\u884c\u73af\u5883\u3002<\/p>\n
# \u5b89\u88c5\u539f\u751f ARM \u7248\u672c Miniconda
\nwget https:\/\/repo.anaconda.com\/miniconda\/Miniconda3-latest-Linux-aarch64.sh
\nbash Miniconda3-latest-Linux-aarch64.sh<\/p>\n
# \u521b\u5efa\u865a\u62df\u73af\u5883
\nconda create -n crnn-ocr python=3.9
\nconda activate crnn-ocr<\/p>\n
# \u5b89\u88c5\u57fa\u7840\u4f9d\u8d56（\u6ce8\u610f：\u5fc5\u987b\u4f7f\u7528 aarch64 \u517c\u5bb9\u5305）
\npip install torch==1.13.1+cpu torchvision==0.14.1+cpu -f https:\/\/download.pytorch.org\/whl\/cpu
\npip install opencv-python flask numpy onnxruntime<\/p>\n
\u26a0\ufe0f \u5173\u952e\u70b9：PyTorch \u5b98\u65b9\u63d0\u4f9b\u9488\u5bf9 aarch64 \u7684 CPU-only wheel \u5305，\u4f46\u90e8\u5206\u7b2c\u4e09\u65b9\u5e93（\u5982 onnxruntime）\u9700\u786e\u8ba4\u662f\u5426\u652f\u6301 ARM64\u3002\u82e5\u4e0d\u53ef\u7528，\u53ef\u8003\u8651\u4ece\u6e90\u7801\u7f16\u8bd1\u6216\u4f7f\u7528\u534e\u4e3a MindSpore \u63d0\u4f9b\u7684\u517c\u5bb9\u5c42\u3002<\/p>\n
\u539f\u59cb\u6a21\u578b\u4e3a .pth \u6743\u91cd\u6587\u4ef6，\u76f4\u63a5\u52a0\u8f7d\u6548\u7387\u8f83\u4f4e\u3002\u6211\u4eec\u91c7\u7528 ONNX \u683c\u5f0f\u5bfc\u51fa + ONNX Runtime \u63a8\u7406\u7684\u65b9\u5f0f\u63d0\u5347\u6027\u80fd\u3002<\/p>\n
# export_to_onnx.py
\nimport torch
\nfrom model import CRNN # \u5047\u8bbe\u5df2\u6709\u5b9a\u4e49<\/p>\n
model = CRNN(img_h=32, nc=1, nclass=37, nh=256)
\nmodel.load_state_dict(torch.load("crnn.pth", map_location="cpu"))
\nmodel.eval()<\/p>\n
dummy_input = torch.randn(1, 1, 32, 128) # \u56fa\u5b9a\u8f93\u5165\u5c3a\u5bf8
\ntorch.onnx.export(
\n model,
\n dummy_input,
\n "crnn.onnx",
\n input_names=["input"],
\n output_names=["output"],
\n dynamic_axes={"input": {0: "batch"}, "output": {0: "seq"}},
\n opset_version=11
\n)<\/p>\n
\u968f\u540e\u5728\u63a8\u7406\u670d\u52a1\u4e2d\u4f7f\u7528 ONNX Runtime：<\/p>\n
import onnxruntime as ort<\/p>\n
# \u52a0\u8f7d ONNX \u6a21\u578b
\nort_session = ort.InferenceSession("crnn.onnx", providers=["CPUExecutionProvider"])<\/p>\n
# \u63a8\u7406\u8c03\u7528
\ndef predict(image_tensor):
\n logits = ort_session.run(None, {"input": image_tensor.numpy()})[0]
\n # CTC decode…
\n return decoded_text<\/p>\n
\u2705 \u6548\u679c\u9a8c\u8bc1：ONNX Runtime \u5728\u9cb2\u9e4f\u4e0a\u6bd4\u539f\u751f PyTorch \u5feb 1.8x，\u4e14 CPU \u5360\u7528\u66f4\u5e73\u7a33\u3002<\/p>\n
Flask \u670d\u52a1\u91c7\u7528 Gunicorn + Nginx \u67b6\u6784\u90e8\u7f72，\u652f\u6301\u5e76\u53d1\u8bf7\u6c42\u5904\u7406\u3002<\/p>\n
# app.py
\nfrom flask import Flask, request, jsonify, render_template
\nimport cv2
\nimport numpy as np<\/p>\n
app = Flask(__name__)<\/p>\n
@app.route('\/')
\ndef index():
\n return render_template('index.html') # WebUI \u9875\u9762<\/p>\n
@app.route('\/api\/ocr', methods=['POST'])
\ndef ocr_api():
\n file = request.files['image']
\n img = cv2.imdecode(np.frombuffer(file.read(), np.uint8), cv2.IMREAD_GRAYSCALE)<\/p>\n
# \u81ea\u52a8\u9884\u5904\u7406
\n img = cv2.resize(img, (128, 32))
\n img = img.astype(np.float32) \/ 255.0
\n img = np.expand_dims(img, axis=(0,1)) # (1,1,32,128)<\/p>\n
text = predict(img)
\n return jsonify({"text": text})<\/p>\n
\u542f\u52a8\u547d\u4ee4：<\/p>\n
gunicorn -w 4 -b 0.0.0.0:5000 app:app –timeout 60<\/p>\n
\u5efa\u8bae：\u9cb2\u9e4f 920 \u62e5\u6709 64 \u6838，\u5efa\u8bae worker \u6570\u8bbe\u7f6e\u4e3a (2 \u00d7 CPU\u6838\u5fc3\u6570) + 1，\u5373\u6700\u591a 8 \u4e2a Gunicorn worker，\u907f\u514d\u8fc7\u5ea6\u7ade\u4e89\u3002<\/p>\n
\u6211\u4eec\u5728\u76f8\u540c\u6a21\u578b\u3001\u76f8\u540c\u8f93\u5165\u6761\u4ef6\u4e0b，\u5bf9\u6bd4\u4e86 Intel Xeon E5-2680 v4 \u4e0e Kunpeng 920 \u7684\u63a8\u7406\u6027\u80fd\u3002<\/p>\n
| \u6307\u6807 | x86_64 (E5-2680) | ARM64 (Kunpeng 920) | \u5dee\u5f02 |
\n|——|——————|———————|——|
\n| \u5355\u56fe\u63a8\u7406\u5ef6\u8fdf（\u5747\u503c） | 780ms | 920ms | +18% |
\n| \u541e\u5410\u91cf（QPS） | 6.4 | 5.2 | -19% |
\n| CPU \u5360\u7528\u7387 | 68% | 73% | +5% |
\n| \u5185\u5b58\u5cf0\u503c | 280MB | 295MB | +5% |<\/p>\n
\u5c3d\u7ba1\u9cb2\u9e4f\u5e73\u53f0\u7565\u6709\u6027\u80fd\u635f\u5931，\u4f46\u4ecd\u5728\u53ef\u63a5\u53d7\u8303\u56f4\u5185\u3002\u66f4\u91cd\u8981\u7684\u662f：
\n– \u5b8c\u5168\u81ea\u4e3b\u53ef\u63a7：\u6446\u8131\u5bf9 Intel + NVIDIA \u751f\u6001\u7684\u4f9d\u8d56
\n– \u529f\u8017\u66f4\u4f4e：Kunpeng 920 TDP \u4ec5 180W，\u4f18\u4e8e\u540c\u7ea7 x86 \u5e73\u53f0
\n– \u56fd\u4ea7\u5316\u5408\u89c4：\u6ee1\u8db3\u4fe1\u521b\u76ee\u5f55\u8981\u6c42，\u9002\u7528\u4e8e\u653f\u5e9c\u3001\u56fd\u4f01\u9879\u76ee<\/p>\n
\u6839\u636e\u672c\u6b21\u9002\u914d\u7ecf\u9a8c，\u603b\u7ed3\u51fa\u4ee5\u4e0b ARM \u67b6\u6784 OCR \u90e8\u7f72\u7684\u6700\u4f73\u5b9e\u8df5：<\/p>\n
\u672c\u6b21 CRNN OCR \u6a21\u578b\u5728\u9cb2\u9e4f ARM \u670d\u52a1\u5668\u4e0a\u7684\u6210\u529f\u90e8\u7f72，\u6807\u5fd7\u7740\u8f7b\u91cf\u7ea7\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5df2\u5177\u5907\u826f\u597d\u7684\u56fd\u4ea7\u786c\u4ef6\u9002\u914d\u80fd\u529b\u3002\u867d\u7136\u5728\u7edd\u5bf9\u6027\u80fd\u4e0a\u4ecd\u7565\u900a\u4e8e\u9ad8\u7aef x86 \u5e73\u53f0，\u4f46\u5176\u7a33\u5b9a\u6027\u3001\u5b89\u5168\u6027\u4e0e\u81ea\u4e3b\u53ef\u63a7\u6027\u4f7f\u5176\u5728\u7279\u5b9a\u884c\u4e1a\u573a\u666f\u4e2d\u5177\u6709\u4e0d\u53ef\u66ff\u4ee3\u7684\u4ef7\u503c\u3002<\/p>\n
\u672a\u6765\u6211\u4eec\u5c06\u8fdb\u4e00\u6b65\u63a2\u7d22：
\n– \u4f7f\u7528 \u534e\u4e3a Atlas 300I \u63a8\u7406\u5361\u7ed3\u5408 AscendCL \u52a0\u901f\u63a8\u7406，\u76ee\u6807 QPS \u63d0\u5347\u81f3 15+
\n– \u5c06 CRNN \u66ff\u6362\u4e3a Transformer-based SAR \u6a21\u578b，\u63d0\u5347\u957f\u6587\u672c\u8bc6\u522b\u7cbe\u5ea6
\n– \u6784\u5efa ARM-native \u6a21\u578b\u8bad\u7ec3 pipeline，\u5b9e\u73b0\u4ece\u8bad\u7ec3\u5230\u90e8\u7f72\u7684\u5168\u94fe\u8def\u56fd\u4ea7\u5316\u95ed\u73af<\/p>\n
📌 \u7ed3\u8bba：ARM \u67b6\u6784\u4e0d\u518d\u662f AI \u63a8\u7406\u7684\u201c\u5907\u9009\u9879\u201d，\u800c\u662f\u9762\u5411\u4fe1\u521b\u3001\u8fb9\u7f18\u8ba1\u7b97\u4e0e\u7eff\u8272\u6570\u636e\u4e2d\u5fc3\u7684\u201c\u4f18\u9009\u65b9\u6848\u201d\u3002\u53ea\u8981\u505a\u597d\u6280\u672f\u9002\u914d\u4e0e\u5de5\u7a0b\u4f18\u5316，CRNN \u8fd9\u7c7b\u7ecf\u5178\u6a21\u578b\u5b8c\u5168\u53ef\u4ee5\u5728\u9cb2\u9e4f\u5e73\u53f0\u4e0a\u53d1\u6325\u51fa\u8272\u8868\u73b0\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
ARM\u67b6\u6784\u9002\u914d\u8fdb\u5c55：CRNN\u6a21\u578b\u5728\u9cb2\u9e4f\u670d\u52a1\u5668\u8fd0\u884c\u6d4b\u8bd5
\n📖 \u9879\u76ee\u7b80\u4ecb
\n\u968f\u7740\u56fd\u4ea7\u5316\u7b97\u529b\u5e73\u53f0\u7684\u5feb\u901f\u53d1\u5c55，ARM \u67b6\u6784\u670d\u52a1\u5668\u5728\u653f\u4f01\u3001\u91d1\u878d\u3001\u80fd\u6e90\u7b49\u5173\u952e\u9886\u57df\u7684\u5e94\u7528\u65e5\u76ca\u5e7f\u6cdb\u3002\u534e\u4e3a\u9cb2\u9e4f\u5904\u7406\u5668\u4f5c\u4e3a\u56fd\u5185\u9886\u5148\u7684 ARMv8 \u67b6\u6784 CPU，\u6b63\u9010\u6b65\u6210\u4e3a AI \u63a8\u7406\u4efb\u52a1\u7684\u91cd\u8981\u627f\u8f7d\u5e73\u53f0\u3002\u7136\u800c，\u7531\u4e8e\u6307\u4ee4\u96c6\u5dee\u5f02\u548c\u751f\u6001\u517c\u5bb9\u6027\u95ee\u9898，\u8bb8\u591a\u6df1\u5ea6\u5b66\u4e60\u6a21\u578b\u5728\u8fc1\u79fb\u81f3 ARM \u73af\u5883\u65f6\u9762\u4e34\u6027\u80fd\u4e0b\u964d\u3001\u4f9d\u8d56\u51b2\u7a81\u751a\u81f3\u65e0\u6cd5\u8fd0\u884c\u7684\u98ce\u9669\u3002
\n\u672c\u6587\u805a\u7126\u4e8e\u4e00\u9879<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"topic":[],"class_list":["post-62814","post","type-post","status-publish","format-standard","hentry","category-server"],"yoast_head":"\n