Okwu mmalite
Mmụba nke ngwaọrụ ihu, dị ka smartphones, smart home devices, na ụgbọ ala kwụụrụ onwe ya, emepụtala mkpa dị mkpa maka itinye ụdị AI nke ọma na ngwaọrụ ndị a. Ụzọ mbugharị ụdị AI ọdịnala na-adaberekarị na akụrụngwa dabere na igwe ojii, nke nwere ike iwebata latency, nchegbu nchekwa, na oke ike oriri. Iji dozie ihe ịma aka ndị a, ngwaike-software ngalaba-ichepụta apụtala dị ka ụzọ na-ekwe nkwa, na-eme ka njikarịcha arụmọrụ ụdị AI, oriri ike, na latency na ngwaọrụ ihu.
Njikọ nke AI, Edge Computing na Co-Design
Edge computing na-ezo aka nhazi nke data na nsọtụ netwọkụ, nso na isi iyi nke data, kama na Centralized ígwé ojii ma ọ bụ data center. Ụdị AI, n'aka nke ọzọ, chọrọ akụrụngwa mgbakọ dị ịrịba ama iji hazie na nyochaa nnukwu data. Njikọ nke AI, mgbakọ ihu, na imekọ ihe na-agụnye njiko njikarịcha ngwaike na ngwa ngwanrọ iji nweta ntinye ụdị AI nke ọma na ngwaọrụ ihu.
Uru nke Akụrụngwa-Software Co-Design
Nhazi akụrụngwa-software na-enye ọtụtụ uru, gụnyere:
** Ọrụ emelitere ***: Site na ịkwalite ngwaike na ngwa ngwanrọ ọnụ, imekọ ihe nwere ike ibute nkwalite arụmọrụ dị ukwuu na ntinye ụdị AI. ** Mbelata oriri ike ***: Nhazi nhazi nwere ike inye aka belata oriri ike site na ịkwalite ngwaike na ngwa ngwanrọ maka ịrụ ọrụ dị ala.
- ** Latency dị ala ***: Nhazi nhazi nwere ike inye aka belata nkwụsị site na ịkwalite ngwaike na ngwa ngwanrọ maka nhazi data na nzikọrịta ozi ngwa ngwa. ** Mmụba mgbanwe ***: ngalaba-iche nwere ike ime ka mmepe nke ụdị AI ndị ọzọ na-agbanwe agbanwe na nke nwere ike ịmegharị ngwa ngwa na ngwaọrụ ihu dị iche iche.
Nlekwasị anya bara uru: Ihe atụ koodu
Ka anyị tụlee otu ihe atụ dị mfe nke ngwaike-software ngalaba-ewe maka AI nlereanya nkenye na onu ngwaọrụ. Anyị ga-eji ọba akwụkwọ Python na-ewu ewu, TensorFlow, iji wepụta ụdị AI bụ isi na Raspberry Pi, ngwaọrụ ihu ama ama.
"Python mbubata tensorflow dị ka tf site na tensorflow.keras mbubata n'ígwé
Kọwaa ụkpụrụ ụlọ AI nlereanya
nlereanya = tf.keras.Sequential([ layers.Conv2D(32, (3, 3), ịgbalite='relu', input_shape=(28, 28, 1)), n'ígwé.MaxPooling2D(2, 2)), oyi akwa.Flatten(), layers.Dense(64, activation='relu'), layers.Dense(10, activation='softmax') ])
Chịkọta ihe nlereanya
model.compile(njikarịcha ='adam', ọnwụ='sparse_categorical_crossentropy', metrics=['nke ziri ezi'])
Budata ihe ndekọ data MNIST
(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
Hazie data ahụ
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
Zụlite ihe nlereanya
model.fit (X_train, y_train, epochs=5, batch_size=128)
Nyochaa ihe nlereanya
test_loss, test_acc = model. nyochaa (X_test, y_test) ebipụta (f'Nnwale izi ezi: {test_acc:.2f}') ``
Ihe atụ koodu a na-egosi ntinye ụdị AI bụ isi na Raspberry Pi site na iji TensorFlow. A zụrụ ihe nlere anya na dataset MNIST wee nyochaa ya na nhazi ule. Koodu ahụ gosipụtara ịdị mfe na mgbanwe nke ngwaike-software co-design maka ntinye ụdị AI na ngwaọrụ ihu.
Top 5-10 Iwu/Snippets kacha eji
Nke a bụ ụfọdụ iwu na snippets ndị a na-ejikarị eme ihe maka nhazi ngwaike-software:
- ** TensorFlow**:
tf.keras.Sequential(),tf.keras.layers.Conv2D(),tf.keras.layers.MaxPooling2D()** Raspberry Pi ***:sudo apt-get update,sudo apt-nweta wụnye python3-pip, `pip3 tinye tensorflow' - Co-Design:
co-design.py -h,co-design.py -m <model_name>, `co-design.py -d <ngwaọrụ_aha>'
Nlere anya
Nke a bụ nleba anya nke uru nke njikwa ngwa ngwa ngwa na iji Chart.js:
chaatị { "ụdị": "ogwe", "data": { "labels": ["Njikwa ihe nlere AI ọdịnala", "Nkọwapụta ngwaike-Software"], "datasets": [ { "label": "Arụmọrụ", "data": [10, 20], "backgroundColor": "rgba(255, 99, 132, 0.2)", "BorderColor": "rgba(255, 99, 132, 1)", "BorderWiidth": 1 }, { "label": "Oriri ike", "data": [50, 30], "backgroundColor": "rgba(54, 162, 235, 0.2)", "BorderColor": "rgba(54, 162, 235, 1)", "BorderWiidth": 1 }, { "label": "Latency", "data": [100, 50], "backgroundColor": "rgba(255, 206, 86, 0.2)", "BorderColor": "rgba(255, 206, 86, 1)", "BorderWiidth": 1 } ] }, "Nhọrọ": { " aha": { "ngosi": eziokwu, "text": "Uru nke Ngwakọta Ngwakọta Ngwaike-Software" }, "ọnụego": { "yAxes": [ { "akara aka": { "beginAtZero": eziokwu } } ] } } }
Ọhụụ a na-atụnyere arụmọrụ, oriri ike, na ịla n'iyi nke mbugharị ụdị AI ọdịnala n'etiti nhazi ngwaike-software.
Mmechi
Ngwakọta ngwa ngwa-ngwaahịa apụtala dị ka ụzọ na-ekwe nkwa maka itinye ụdị AI nke ọma na ngwaọrụ ihu. Site n'ịkwalite ngwaike na ngwa ngwa ọnụ ọnụ, imekọ ihe nwere ike bute nkwalite arụmọrụ dị ukwuu, ibelata oriri ike, yana obere oge. Edemede a enyela nkọwa zuru ezu nke uru na ngwa bara uru nke nhazi ngwaike-software, gụnyere ihe atụ koodu dị mfe na nhụta uru. Ka ọchịchọ nke ihu AI na-aga n'ihu na-eto eto, ngwaike-software ngalaba-ichepụta nwere ike ịrụ ọrụ dị mkpa na-arịwanye elu na mmepe nke ụdị AI dị mma ma dị irè.