Sold and deployed almost entirely inside mainland China. Huawei is on the US Entity List and Ascend silicon is export-controlled wherever it is located, so it cannot be rented from any provider on this site. Superseded for new designs by the Ascend 310P. Huawei publishes no product page for the 310 chip itself: the figures here come from its own Hot Chips 31 architecture disclosure of August 2019.
Huawei logo

Huawei Ascend 310

Type: NPUArchitecture: Da VinciForm factor: SoCReleased: 2018Process: 12nmSpec confidence: Official
TDP
8 W

Overview

The Huawei Ascend 310, codenamed Ascend-Mini, was the first Ascend processor to ship and the chip behind the entire Atlas 200 and Atlas 500 edge line. Announced by Eric Xu at Huawei Connect 2018, it pairs two Da Vinci AI cores with eight Arm Cortex-A55 host cores on a 12nm die of 104.4 square millimetres, and delivers 8 TFLOPS of FP16 or 16 TOPS of INT8 inside an 8 watt envelope, which is the efficiency point the whole design was built around. Alongside the AI cores it carries a substantial fixed-function media block, 16 channels of H.264 and H.265 decode plus one channel of encode, and an 8 MB on-chip buffer feeding the matrix units. Memory is 128-bit LPDDR4X, but capacity and bandwidth are deliberately left blank here because they are a board-level choice rather than a chip specification: Huawei's own Atlas 200 module ships the same silicon with either 4 GB or 8 GB. The Da Vinci Cube in this generation implements 4,096 FP16 MACs and 8,192 INT8 MACs and has no FP32 or BF16 matrix path at all, which is why this part carries only FP16 and INT8 figures.

Performance

Peak theoretical throughput by precision type

PrecisionPeak
FP64
No verified data available
FP32
No verified data available
TF32
No verified data available
BF16
No verified data available
FP16
16-bit floating point
8TFLOPS
FP8
No verified data available
FP6
No verified data available
FP4
No verified data available
INT8
8-bit integer
16TOPS

The Ascend 310 in the GPU landscape

Peak FP16 TFLOPS (dense) against TDP, single-GPU parts tracked by Flopper

06001,2001,8002,4003,0000 W250 W500 W750 W1000 W1250 W1500 WInstinct MI355XAscend 310

Higher and further left is better: more half-precision throughput for less power.

Specifications

Architecture

Da Vinci

Form Factor

SoC

Launch Year

2018

Process Node

12nm

Memory

No verified data available LPDDR4X

Bandwidth

No verified data available

TDP

8 W

Max power

8 W

Da Vinci AI Core

2

Spec Confidence

Official

Full Specifications

Compute Engine
Da Vinci AI Core 2
Chip Design
Die Size 104 mm²
Process Node 12nm
Memory
Memory No verified data available
Memory Type LPDDR4X
Bandwidth No verified data available
Interface Width 128-bit
Interconnect & I/O
PCIe 3.0 x4
Cache
L2 Cache 3 MB
Power & Thermal
TDP 8 W
Max power 8 W
Enterprise Features
Compute APIs CANN, MindSpore, MindX, ACL
General
Form Factor SoC
Architecture Da Vinci
Process Node 12nm
Launch Year 2018

Datasheet & Resources

Data Provenance

Every figure traced to a source

Primary Source

Publisher
Huawei
Published
2019-08-19

Data Quality

Spec confidence
Official
Clock basis
Boost
Core precisions with figures
2 of 9
Normalization
All values in TFLOPS

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Frequently Asked Questions

How many TFLOPS does the Huawei Ascend 310 have?

The Huawei Ascend 310 delivers 8 TFLOPS FP16 at peak. Flopper does not currently have verified FP32 and FP8 throughput figures for it.

What is the power consumption of the Huawei Ascend 310?

The Huawei Ascend 310 has a TDP (Thermal Design Power) rating of 8 watts.

How much memory does the Huawei Ascend 310 have?

Flopper does not currently have verified memory capacity or bandwidth figures for the Huawei Ascend 310.

What architecture is the Huawei Ascend 310 based on?

The Huawei Ascend 310 is based on the Da Vinci architecture, launched in 2018.

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