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'''Machine Learning Unit''' ('''MLU''') is a family of [[neural processors]] designed by [[Cambricon]]. | '''Machine Learning Unit''' ('''MLU''') is a family of [[neural processors]] designed by [[Cambricon]]. | ||
| + | |||
| + | == Overview == | ||
| + | Announced in late 2017, the MLU family of [[neural processors]] designed for cloud-based workloads for both inference and training. In contrast to Cambricon's mobile and edge computing [[IP cores]], those processors have a higher power envelope and are designed for much higher performance. | ||
| + | |||
| + | == Models == | ||
| + | {{empty section}} | ||
| + | |||
| + | |||
| + | * {{\|MLU100}} | ||
| + | * {{\|MLU200}} | ||
| + | |||
| + | == See also == | ||
| + | * Google's {{google|TPU}} | ||
Latest revision as of 01:08, 27 May 2018
| MLU | |
| Developer | Cambricon |
| Manufacturer | TSMC |
| Type | Neural Processors |
| Introduction | Nov 7, 2017 (announced) May 3, 2018 (launch) |
| ISA | MLU |
| Word size | 64 bit 8 octets
16 nibbles |
| Process | 16 nm 0.016 μm
1.6e-5 mm |
| Technology | CMOS |
| Clock | 1,000 MHz-1,300 MHz |
Machine Learning Unit (MLU) is a family of neural processors designed by Cambricon.
Overview[edit]
Announced in late 2017, the MLU family of neural processors designed for cloud-based workloads for both inference and training. In contrast to Cambricon's mobile and edge computing IP cores, those processors have a higher power envelope and are designed for much higher performance.
Models[edit]
 |
This section is empty; you can help add the missing info by editing this page. |
See also[edit]
- Google's TPU
Facts about "Machine Learning Unit (MLU) - Cambricon"
| designer | Cambricon + |
| first announced | November 7, 2017 + |
| first launched | May 3, 2018 + |
| full page name | cambricon/mlu + |
| instance of | integrated circuit family + |
| instruction set architecture | MLU + |
| main designer | Cambricon + |
| manufacturer | TSMC + |
| name | MLU + |
| process | 16 nm (0.016 μm, 1.6e-5 mm) + |
| technology | CMOS + |
| word size | 64 bit (8 octets, 16 nibbles) + |