Ifihan

Ilọsiwaju ti awọn ẹrọ eti, gẹgẹbi awọn fonutologbolori, awọn ẹrọ ile ọlọgbọn, ati awọn ọkọ ayọkẹlẹ adase, ti ṣẹda iwulo titẹ fun imuṣiṣẹ awoṣe AI daradara lori awọn ẹrọ wọnyi. Awọn ọna imuṣiṣẹ awoṣe AI ti aṣa nigbagbogbo dale lori awọn amayederun orisun-awọsanma, eyiti o le ṣafihan lairi, awọn ifiyesi aabo, ati agbara agbara giga. Lati koju awọn italaya wọnyi, apẹrẹ-apẹrẹ hardware-software ti farahan bi ọna ti o ni ileri, ṣiṣe iṣapeye ti iṣẹ awoṣe AI, lilo agbara, ati lairi lori awọn ẹrọ eti.

Ikorita ti AI, Edge Computing, ati Apẹrẹ Ajọpọ

Iširo Edge n tọka si sisẹ data ni eti nẹtiwọọki, isunmọ si orisun data, dipo awọsanma aarin tabi ile-iṣẹ data. Awọn awoṣe AI, ni ida keji, nilo awọn orisun iṣiro pataki lati ṣe ilana ati itupalẹ awọn oye nla ti data. Ikorita ti AI, iširo eti, ati apẹrẹ-apẹrẹ pẹlu iṣapeye apapọ ti ohun elo ati awọn paati sọfitiwia lati ṣaṣeyọri imuṣiṣẹ imuṣiṣẹ awoṣe AI daradara lori awọn ẹrọ eti.

Awọn anfani ti Hardware-Software Co-Design

Apẹrẹ-apẹrẹ hardware-software nfunni ni ọpọlọpọ awọn anfani, pẹlu:

** Imudara iṣẹ ***: Nipa jijẹ ohun elo hardware ati awọn paati sọfitiwia papọ, apẹrẹ-apẹrẹ le ja si awọn ilọsiwaju iṣẹ ṣiṣe pataki ni imuṣiṣẹ awoṣe AI.

** Idinku agbara agbara ***: Apẹrẹ-apẹrẹ le ṣe iranlọwọ lati dinku agbara agbara nipasẹ jijẹ ohun elo hardware ati awọn paati sọfitiwia fun iṣẹ agbara kekere. ** Lairi kekere ***: Apẹrẹ-apẹrẹ le ṣe iranlọwọ lati dinku aiduro nipa mimuṣiṣẹpọ hardware ati awọn paati sọfitiwia fun sisẹ data iyara ati ibaraẹnisọrọ. ** Irọra ti o pọ sii ***: Apẹrẹ-apẹrẹ le jẹ ki idagbasoke ti awọn awoṣe AI rọ diẹ sii ati adaṣe ti o le ni irọrun gbe lọ lori awọn ẹrọ eti oriṣiriṣi.

Idojukọ Wulo: Apeere koodu

Jẹ ki a wo apẹẹrẹ ti o rọrun ti apẹrẹ-apẹrẹ hardware-software fun imuṣiṣẹ awoṣe AI lori awọn ẹrọ eti. A yoo lo ibi-ikawe Python olokiki, TensorFlow, lati ran awoṣe AI ipilẹ kan sori Rasipibẹri Pi, ẹrọ eti olokiki kan.

`` Python agbewọle tensorflow bi tf lati tensorflow.keras gbe wọle fẹlẹfẹlẹ

Ṣetumo faaji awoṣe AI

awoṣe = tf.keras.Sequential ([ layers.Conv2D(32, (3, 3), ibere ise='relu', input_shape=(28, 28, 1)), Layer.MaxPooling2D((2, 2)), Layer.Flatten (), layers.Dense(64, activation='relu'), layers.Dense(10, ibere ise='softmax') ])

Ṣe akopọ awoṣe naa

model.compile(optimizer='adam', pipadanu='sparse_categorical_crossentropy', metiriki=['ipe'])

Kojọpọ data MNIST

(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data ()

Ṣe ilana data naa tẹlẹ

X_train = X_train.reshape (-1, 28, 28, 1).astype ('float32') / 255.0 X_test = X_test.reshape (-1, 28, 28, 1).astype ('float32') / 255.0

Ṣe ikẹkọ awoṣe

model.fit (X_train, y_train, epochs=5, batch_size=128)

Ṣe iṣiro awoṣe naa

test_loss, test_acc = model.iyẹwo(X_test, y_test) titẹ (f'Test_acc:.2f}')


Apẹẹrẹ koodu yii ṣe afihan imuṣiṣẹ imuṣiṣẹ awoṣe AI ipilẹ kan lori Rasipibẹri Pi nipa lilo TensorFlow. Awoṣe naa jẹ ikẹkọ lori ipilẹ data MNIST ati iṣiro lori eto idanwo kan. Koodu naa ṣe afihan ayedero ati irọrun ti apẹrẹ-apẹrẹ hardware-software fun imuṣiṣẹ awoṣe AI lori awọn ẹrọ eti.

## Top 5-10 Awọn aṣẹ ti a lo julọ / awọn snippets

Eyi ni diẹ ninu awọn aṣẹ ti o wọpọ julọ ti a lo ati awọn snippets fun apẹrẹ-apẹrẹ-software:

* **TensorFlow**: `tf.keras.Sequential()`, `tf.keras.layers.Conv2D()`, `tf.keras.layers.MaxPooling2D()`
** Rasipibẹri Pi ***: `sudo apt-get update`, `sudo apt-gba fi sori ẹrọ python3-pip`, `pip3 fi tensorflow sori ẹrọ`
* ** Apẹrẹ Ajọpọ **: `co-design.py -h`, `co-design.py -m <model_name>`, `àjọ-apẹrẹ.py -d <orukọ_ẹrọ>`

## Wiwo

Eyi ni iwoye ti awọn anfani ti apẹrẹ-apẹrẹ hardware-software nipa lilo Chart.js:

`` aworan apẹrẹ
{
  "type": "ọgọ",
  "data": {
    "labels": ["Ifiranṣẹ Awoṣe AI Ibile", "Aṣaṣe-Aṣeṣe-Software Hardware"],
    "awọn ipilẹ data": [
      {
        "label": "Iṣẹ́",
        "data": [10, 20],
        "backgroundColor": "rgba(255, 99, 132, 0.2)",
        "borderColor": "rgba(255, 99, 132, 1)",
        "AalaWidth": 1
      },
      {
        "label": "Gbigba agbara",
        "data": [50, 30],
        "backgroundColor": "rgba(54, 162, 235, 0.2)",
        "borderColor": "rgba(54, 162, 235, 1)",
        "AalaWidth": 1
      },
      {
        "label": "Latency",
        "data": [100, 50],
        "backgroundColor": "rgba(255, 206, 86, 0.2)",
        "borderColor": "rgba(255, 206, 86, 1)",
        "AalaWidth": 1
      }
    ]
  },
  "awọn aṣayan": {
    "akọle": {
      "ifihan": otitọ,
      "text": "Àwọn ànfàní ti Àjọ-Àgbékalẹ̀ Àkópọ̀ Software Hardware-Software"
    },
    "iwọn": {
      "yAxes": [
        {
          "awọn ami": {
            "beginAtZero": otitọ
          }
        }
      ]
    }
  }
}

Iwoye yii ṣe afiwe iṣẹ ṣiṣe, agbara agbara, ati airi ti imuṣiṣẹ awoṣe AI ti aṣa dipo apẹrẹ-apẹrẹ hardware-software.

Ipari

Hardware-software àjọ-apẹrẹ ti farahan bi ọna ti o ni ileri fun imuṣiṣẹ awoṣe AI daradara lori awọn ẹrọ eti. Nipa jijẹ ohun elo hardware ati awọn paati sọfitiwia papọ, apẹrẹ-apẹrẹ le ja si awọn ilọsiwaju iṣẹ ṣiṣe pataki, idinku agbara agbara, ati aipe kekere. Nkan yii ti pese alaye alaye ti awọn anfani ati awọn ohun elo ti o wulo ti apẹrẹ-apẹrẹ hardware-software, pẹlu apẹẹrẹ koodu ti o rọrun ati iwoye ti awọn anfani. Bi ibeere fun eti AI n tẹsiwaju lati dagba, apẹrẹ-apẹrẹ sọfitiwia hardware le ṣe ipa pataki ti o pọ si ni idagbasoke awọn awoṣe AI ti o munadoko ati imunadoko.

Ṣiṣe imuṣiṣẹ Awoṣe AI ti o munadoko lori Awọn ẹrọ Edge nipasẹ Apejọ-apẹrẹ Hardware-Software