** Nbunye Model AI nke ọma na ngwaọrụ Edge site na ngwa ngwa-Software Co-Design ***

** Okwu mmalite**

Mgbasawanye nke ngwaọrụ ihu ewepụtala ohere ọhụrụ maka ngwa Artificial Intelligence (AI) ka ebuga ya ozugbo, na-enye ngwa dị ka ụlọ smart, ụgbọ ala kwụụrụ onwe ya, na akpaaka ụlọ ọrụ. Agbanyeghị, akụrụngwa mgbakọ na mwepụ nwere oke na mgbochi ike nke ngwaọrụ ihu na-ebute nnukwu ihe ịma aka maka ibunye ụdị AI. N'isiokwu a, anyị ga-enyocha njikọ nke AI, injinịa ngwanrọ, na ihe owuwu iji kwalite arụmọrụ ngwaọrụ, na-eme ka ọganihu na-adịbeghị anya na arụmọrụ AI na usoro nhazi ngwaike-software.

** Ihe ịma aka nke ntinye ụdị AI na ngwaọrụ Edge ***

A na-eji akụrụngwa mgbako nwere oke, gụnyere CPU, ebe nchekwa na oriri ike. Ụdị AI ọdịnala, nke a na-emekarị maka ntinye nke dabeere na ígwé ojii, adịghị ahazi ya maka ngwaọrụ ihu. N'ihi ya, ha nwere ike ibute:

• ** Mmebi arụmọrụ ***: Ụdị AI nwere ike ọ gaghị arụ ọrụ nke ọma na ngwaọrụ ihu, na-eduga n'oge nzaghachi ngwa ngwa yana ibelata izi ezi. ** oriri ike ***: Ụdị AI nwere ike iri ike dị egwu, nke nwere ike bụrụ nchegbu maka ngwaọrụ ihu batrị kwadoro. ** Ihe mgbochi ebe nchekwa ***: ụdị AI nwere ike ịchọ nnukwu ebe nchekwa, nke nwere ike bụrụ ihe ịma aka maka ngwaọrụ ihu nwere oke ebe nchekwa.

** Usoro nhazi nhazi ngwaike-Software ***

Iji dozie ihe ịma aka ndị a, usoro nhazi nhazi ngwaike-software apụtala dị ka ihe ngwọta na-ekwe nkwa. Usoro ndị a gụnyere imepụta ma ngwaike na ngwa sọftụwia nke ngwaọrụ ihu na tandem, na-ebuli ha elu maka ntinye ụdị AI.

1. Mpempe akwụkwọ nlereanya

Ịkpụcha ihe nlereanya gụnyere iwepụ ihe dị arọ na njikọ na-adịghị mkpa site na ụdị AI, na-ebelata nha ya na ihe mgbagwoju anya. Enwere ike itinye usoro a na netwọkụ akwara ozi na osisi mkpebi.

"Python mbubata ọwa mbubata torch.nn ka nn

ModelPruning klas (nn.Module): def init(onwe): ibu (ModelPruning, onwe).init() self.fc1 = nn.Linear(784, 128) self.fc2 = nn.Linear (128, 10)

gbapụ n'ihu (onwe, x):
    x = ọkụ.relu (self.fc1 (x))
    x = onwe.fc2 (x)
    laghachi x

nlereanya = ModelPruning() pruned_model = torch.nn.utils.prune_l1_unstructured(ụdị, 'ibu', 0.1) ``

2. Mgbasa ihe ọmụma

Ntugharị ihe ọmụma gụnyere ịnyefe ihe ọmụma site na nnukwu ụdị AI dị mgbagwoju anya gaa na nke nta, dị mfe. Enwere ike iji usoro a iji belata nha na mkpa mgbakọ na mwepụ nke ụdị AI.

"Python mbubata ọwa mbubata torch.nn ka nn

Ngosi ihe ọmụma klaasị(nn.Module): def init(onwe): ibu (KnowledgeDistillation, onwe).init() self.fc1 = nn.Linear(784, 128) self.fc2 = nn.Linear (128, 10)

gbapụ n'ihu (onwe, x):
    x = ọkụ.relu (self.fc1 (x))
    x = onwe.fc2 (x)
    laghachi x

teacher_model = ModelPruning() student_model = ỌmụmaDistillation() ``

3. Ọnụ ọgụgụ

Quantization gụnyere ibelata izizi nke ihe atụ AI na mmemme, na-ebelata ihe mgbako chọrọ nke ụdị AI.

"Python mbubata ọwa mbubata torch.nn ka nn

klaasị Quantization(nn.Module): def init(onwe): super(Quantization, self).init() self.fc1 = nn.Linear(784, 128) self.fc2 = nn.Linear (128, 10)

gbapụ n'ihu (onwe, x):
    x = ọkụ.relu (self.fc1 (x))
    x = onwe.fc2 (x)
    laghachi x

nlereanya = ModelPruning() quantized_model = torch.quantization.quantize_dynamic (ụdị, {torch.nn.Linear}, dtype=torch.qint8) ``

** Iwu 5-10 Kachasị Eji mee ihe

** Nlegharị anya ***

chaatị { "ụdị": "ogwe", "data": { "labels": ["Ụdị ịkwachaa", "Nkọwa ihe ọmụma", "Quantization"], "datasets": { "label": "Izi ezi", "data": [0.9, 0.85, 0.8] } }, "Nhọrọ": { " aha": { "ngosi": eziokwu, "text": "Ntụnyere ziri ezi" }, "akụkọ": { "ngosi": eziokwu } } }

Mkpebi

Nbunye ụdị AI nke ọma na ngwaọrụ ihu chọrọ ngwakọta nke ngwaike na usoro nhazi ngwanrọ. Ịkọwa ihe nlereanya, ntugharị ihe ọmụma, na quantization bụ ụfọdụ n'ime usoro a ga-eji belata nha na mgbakọ na mwepụ chọrọ nke ụdị AI. Site n'iji usoro ndị a eme ihe, ndị mmepe nwere ike ịmepụta ụdị AI emebere maka ngwaọrụ ihu, na-eme ngwa ngwa dị ka ụlọ smart, ụgbọ ala kwụụrụ onwe ya na akpaaka ụlọ ọrụ.

** Ọrụ n'ọdịnihu ***

Ọrụ n'ọdịnihu na mpaghara a gụnyere:

** Imepe usoro nhazi ngwaike-software ọhụrụ ***: Ndị nyocha na ndị mmepe nwere ike inyocha usoro ọhụrụ maka ịkwalite ụdị AI maka ngwaọrụ ihu.

• **Mmelite ụdị ịkwachaa na ihe ọmụma distillation algọridim ***: Imepụta algọridim dị mma maka ịkpụcha ihe nlereanya na nhụsianya ihe ọmụma nwere ike ibute arụmọrụ na izi ezi. ** Nyochaa iji ụdị AI ndị ọzọ ***: Ụdị AI ndị ọzọ, dị ka osisi mkpebi na oke ọhịa na-enweghị usoro, nwere ike nyochaa maka ntinye ngwaọrụ.

Site n'ịga n'ihu na-aga n'ihu na ngalaba nke ngwa-ngwa-ngwa-ngwa-ngwa-ngwa-ngwa-ngwa-ngwa, anyị nwere ike ịmepụta ụdị AI dị mma ma dị irè maka ngwaọrụ ihu, na-eme ka ọtụtụ ngwa ngwa na iji okwu mee ihe.