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Tensor Cores inyanzvi dzehurdware mukati memazuva ano NVIDIA GPUs anoita matrix kuwanda-uye-kuunganidza mashandiro nekukurumidza.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
They are the main reason a single GPU can train and run large neural networks orders of magnitude faster than general-purpose compute would allow.
Yakaunzwa neVolta architecture muna 2017, Tensor Cores masekete akatsaurirwa anokokorodza diki matrix kuwanza pamwe nekuwedzera (D = A x B + C) mukushanda kumwe chete, pane kuita imwe neimwe kuwanda imwe panguva pane yakajairwa CUDA cores. Nekuti ingangoita yese layer yeneural network inodzikisira kuita matrix kuwanda, izvi zvinoenderana nemasvomhu AI anotoda. Chizvarwa chega chega cheGPU chakawedzera zvavanobata: Volta akaita 4x4 FP16 mataira, ukuwo Ampere, Hopper, uye Blackwell zvivakwa zvakawedzera akaderera-chaiyo mafomati seTF32, BF16, INT8, FP8, uye FP4. Kunyatsodzika kwakadzika kunoreva nhamba dzakawanda dzakagadziriswa pawachi imwe neimwe, zvichiwedzera kuwedzera kwekudzidzira uye kufungidzira uku uchichengeta chokwadi chinogamuchirwa.
Kudzidzira mamodheru emitauro mikuru seGPT-maitiro ekushandura, uko mabhiriyoni ematrix kuwanda padanho rinomhanya paTensor Cores muBF16 kana FP8.
Kumhanyisa chaiyo-nguva inference yechatbots uye majenareta emifananidzo, uchishandisa INT8 kana FP8 quantization yekushandira vashandisi vazhinji paGPU.
Kumhanyisa NVIDIA DLSS mumitambo yemavhidhiyo, uko neural network inokwidza yakaderera-resolution mafuremu uchishandisa Tensor Cores furemu yega yega.
Kumhanyisa komputa yesainzi senge protein-kupeta (AlphaFold) uye mamiriro ekunze akagadziridzwa sematrix-inorema neural workloads.
Tensor Cores anoramba achifamba akananga kunogara-pasi-chaiyo: Hopper yakawedzera FP8 uye Blackwell yakaunza 4-bit FP4 ine hardware-inogadziriswa scaling, ingangoita kaviri kubuda nhanho imwe neimwe yekufunga-inorema basa rekuita. Tarisira kusimba kwerutsigiro rwe sparsity (kusvetuka zero huremu), microscaling mafomati anonamira zvikero kune zvidiki zvidhinha zvenhamba, uye yakadzama yekubatanidza nemamemory system kuitira kuti macores arambe akadyiswa. Sezvo mamodheru achikura, iyo matrix injini, kwete mbishi wachi yekumhanya, inoramba iri yepakati nhandare yeAI hardware kuita.
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Tensor Cores inyanzvi dzehurdware mukati memazuva ano NVIDIA GPUs anoita matrix kuwanda-uye-kuunganidza mashandiro nekukurumidza. Ndivo chikonzero chikuru chekuti GPU imwe chete inogona kudzidzisa uye kumhanya hombe neural network maodha ehukuru nekukurumidza kupfuura general-chinangwa compute ingabvumira.
Tensor Cores anoita yakasanganiswa matrix kuwanza uye kuunganidza mudanho rimwe, ndiko kunoshanda chaiko kunotonga neural network layer.
Tensor Cores yakatanga neVolta architecture muna 2017, kutanga neFP16 4x4 matrix mashandiro.
Kushandisa mabhiti mashoma panhamba kunoreva kuwanda kwehukoshi kuyerera kuburikidza ne Hardware kutenderera kwega kwega, zvakanyanya kuwedzera kumhanya nediki chete, kazhinji inoshiviririka, kurasikirwa kwechokwadi.
Zvinopinza zvinogona kunge zvakaderera, asi accumulator inowanzo mhanya nepamusoro-soro seFP32 kudzikamisa kuunganidzwa kwekukanganisa kwekutenderera.
Maraibhurari ari pasi anopwanya mashandiro makuru ematrix muTensor-Core-saizi mataira otomatiki, saka masisitimu anowana kukurumidza pasina manyorero ekunyora.
Ramba uchidzidza
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InoteveraGaidhi rinotevera
Core ML for On-Device Apple Deployment
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