HiSilicon Kirin 710A vs JLQ JR510 vs HiSilicon Kirin 970

HiSilicon Kirin 710A

The HiSilicon Kirin 710A is a ARM-based mid-range Octa-Core-SoC for Android based smartphones and tablets. It is based on the Kirin 710, but manufactured in 14nm FinFET at SMIC (China) versus the TSMC in 12nm. It was announced mid 2020 and contains 8 processor cores (4x Cortex-A73 at up to 2 GHz, 4x Cortex-A53 at up to 1.7 GHz, BigLITTLE), an ARM Mali G51 MP4, a dual-channel LPDDR4 memory controller and a LTE radio (600 Mbps down, 150 Mbps up). Therefore, the CPU part is similar to the Kirin 960 and 970 (both higher clocked), but the graphics card is a lot slower. 

JLQ JR510

The JLQ JR510 is a lower mainstream SoC for (Android-based) smartphones and tablets by JLQ, a joint venture with Qualcomm and several Chinese companies.

The JLQ JR510 integrates eight ARM Cortex-A55 cores (similar to the Snapdragon 439) in two Clusters. 4 higher-clocked cores with up to 2 GHz and four power saving cores with up to 1.5 GHz.

The integrated graphics card is not an Adreno, but a ARM Mali G52MP1 (single cluster). Displays can be with resolutions up to 1080p/60Hz, for 720p 90 Hz is possible.

The JR510 also includes an AI accelerator with a theoretical performance of up to 1.23 TOPS. For connectivity, the SoC supports 4G (Cat4 / 7), Wi-Fi 5 (802.11ac), Bluetooth 5.0, FM and GNSS.

HiSilicon Kirin 970

The HiSilicon Kirin 970 is an ARM based high-end octa-core SoC for smartphones and tablet, which was introduced with the Huawei Mate 10 in late 2017. Besides 8 CPU cores (4x Cortex-A73, 4x Cortex-A53), the SoC is also equipped with a modern Mali-G72 MP12 graphics adapter, a dual-channel LPDDR4 memory controller as well as an LTE (4x4 MIMO, 5-band-carrier-aggregation, QAM-256, up to 1.2 Gbit/s) modem.

The performance cluster is clocked at up to 2.4 GHz and the four A53 power saving cores with up to 1.8 GHz. The performance should improve by about 20 percent according to Huawei and the power consumption reduced by 20 percent.

On the chip HiSilicon also included a dedicated Neural Processing Unit (NPU) for running machine learning tasks very efficiently compared to the CPU cores.