SL1680 Embedded IoT Processor

Product Overview

The Synaptics SL-Series combines multiple functions into a single system on chip (SoC) with Linux® and Android support. It features hardware accelerators for edge inferencing, data security, and audio, video and graphics handling.

The SL1680 leverages a powerful GPU for advanced graphics and AI acceleration, quad-core Arm® Cortex®-A73 64-bit CPU subsystem performance, a multi-TOPS NPU and multimedia accelerators for image signal processing (ISP), 4K video encode/decode and audio. These compute engines enable the embedded processor to power consumer, industrial and enterprise IoT workloads.

As part of the Synaptics Astra platform, the SL1680 delivers a seamless experience combining standards-based open-source software frameworks, powerful AI tools and Synaptics’ superior wireless solutions.

 

Documentation


Type Title Format Revision Revision Date
Product Brief SL1680 EMBEDDED IOT PROCESSOR PRODUCT BRIEF PDF D
Datasheet SL1680 EMBEDDED IOT PROCESSOR ELECTRICAL SPECIFICATION DATASHEET PDF E
Datasheet SL1680 EMBEDDED IOT PROCESSOR: FUNCTIONAL SPECIFICATION PDF B
Manual Guidance or TRM ASTRA MACHINA SL1680 DEVELOPER KIT USER GUIDE PDF E
Application Note ASTRA MACHINA FOUNDATION PWM APP NOTE PDF C
Application Note ASTRA MACHINA FOUNDATION SD AND SDIO HOST APPLICATION NOTE PDF B
Application Note ASTRA MACHINA FOUNDATION SERIES CAN DRIVER MODULE HW CONNECTION APPLICATION NOTE PDF C
Application Note ASTRA MACHINA FOUNDATION SERIES CONNECTION OF A KEYPAD APPLICATION NOTE PDF D
Application Note ASTRA MACHINA FOUNDATION SERIES EMMC APPLICATION NOTE PDF D
Application Note ASTRA MACHINA FOUNDATION SERIES GENERAL MIPI DSI LCM HARDWARE CONNECTION APPLICATION NOTE PDF C
Application Note ASTRA MACHINA FOUNDATION SERIES I2S, PDM INTERFACES APPLICATION NOTES PDF C
Application Note ASTRA MACHINA FOUNDATION SERIES MIPI-CSI CAMERA HARDWARE CONNECTION APP NOTE PDF E
Application Note ASTRA MACHINA FOUNDATION SERIES MIPI-DSI2 HDMI HARDWARE CONNECTION APP NOTE PDF C
Application Note ASTRA MACHINA FOUNDATION SERIES POE HARDWARE CONNECTION PDF B
Application Note ASTRA MACHINA FOUNDATION SERIES RGB LCM HARDWARE CONNECTION APP NOTE PDF B
Application Note ASTRA MACHINA FOUNDATION SERIES SL16X0 SOC INTERFACE TO 10BASE-T1S ETHERNET APPLICATION NOTE PDF A
Application Note ASTRA MACHINA FOUNDATION SERIES TWSI APPLICATION NOTE PDF B
Application Note ASTRA MACHINA FOUNDATION SERIES UART APPLICATION NOTE PDF C
Application Note ASTRA MACHINA FOUNDATION SL1680 PRODUCT LIFETIME APPLICATION NOTE PDF A
Application Note ASTRA MACHINA SERIES: POWER MANAGEMENT INTERGRATED CIRCUIT (PMIC) SOLUTION AND BILL OF MATERIAL (BOM) PDF B
Application Note ASTRA MACHINA SL-SERIES POWER CONSUMPTION APPLICATION NOTE PDF C
Application Note ASTRA™ MACHINA FOUNDATION SERIES SPI APPLICATION NOTE PDF B
Application Note GENERAL ESD/EOS CONTROL METHODS APPLICATION NOTE PDF B
Application Note GENERAL GUIDE FOR SOLDERING SMD TO PC BOARDS APPLICATION NOTE PDF C
Application Note SL1680 GENERAL PCB DESIGN AND LPDDR4 & LPDDR4(X) INTERFACE LAYOUT GUIDELINES PDF E
Board Related RELEASE PACKAGE, SL1680 RDK SCHEMATIC CORE MODULE ZIP E
Board Related RELEASE PACKAGE, SL16X0 RDK IO SCHEMATIC BOARD ZIP E
Quality Report SL1680 SILICON PRODUCT QUALIFICATION REPORT (CONSUMER + INDUSTRIAL GRADE) PDF F
General SL1680 QUALIFIED COMPONENT LIST PDF L
AI-Native Edge SOC
High Performance Per Watt
Dual Camera Support
Proven Security Model

Features

Compute, Memory, AI
  • Quad-core Arm® Cortex®-A73 @ 2.1GHz
  • Cache: 64KB I-cache and 32KB D-cache per core,1MB shared L2
  • DRAM Controller: 64-bit LPDDR4/LPDDR4x-3733
  • 7.9+ TOPS secure NPU with support for multiple DNN frameworks
GFX, Vision, Display
  • GPU: Imagination™ PowerVR™ Series9XE GE9920
  • Multi-standard video decoding with AV1, H.265/264, VP9/8, MPEG2
  • Multi-stream encoding for H.264, VP8
  • Simultaneous 2160p60 decode and 1080p60 encode
  • OpenGL® ES™ up to 3.2, DirectFB, OpenCL™ 1.2, Vulkan® 1.1, Android™ NN
Audio
  • Far-field voice, keyword detection, decompression, post-processing
Security
  • Secure CPU, Secure Boot with RSA, memory scrambling
  • Integrity checking, physical attack mitigation, true random number generator
Connectivity, I/O, Sensing
  • GbE networking, PCIe, eMMC 5.1, SPI, SDIO, UART, USB, GPIO, ADCs
  • Always-on domain for multi-protocol wake-up events

Benefits

  • Multi-modal IoT SoC lowers system cost
  • Powerful NPU enables out-of-the-box AI
  • Pairs with best-in-class Synaptics connectivity
  • Enables fast time to market

Applications

  • Smart appliances
  • Home security gateways
  • Industrial control systems
  • Signage and displays
  • Point-of-sale systems
  • Scanners

Related Products

  • SL1620

    The SL1620 is ideal for applications that need robust processing, powerful AI capability and 3D graphics. This chip supports Linux® OS, exceptional audio algorithms, several peripherals, dual displays, companion Synaptics SoC for connectivity, and audio front end.

  • SL1640

    The SL1640 is a cost- and power-optimized embedded processor with superior compute engines, including a quad-core Arm® Cortex®-A55, 1.6+ TOPS NPU, Imagination GE9920 GPU with 90 GFLOPS 16-bit operations, Ultra HD video encode and decode pipelines and audio DSP.

  • Astra™ Machina™

    Astra Machina is a modular, reliable developer kit for Synaptics' high-performing embedded processors. It incorporates superior AI capabilities, advanced multimedia processing and secure hardware, allowing fast prototyping.

Transform Your AI Journey

Accelerate Development With On-Device AI

With built-in AI models, you accelerate smart-feature deployment on your IoT products. Harness powerful embedded AI tools to easily adapt your own PyTorch, TensorFlow Lite or ONNX AI models for on-device AI accelerators.

Achieve Rapid Prototyping With Astra Machina 

Unlock the full potential of the SL1680 with the Astra Machina Foundation. This developer-friendly platform kit offers AI, wireless connectivity and compute tools for hassle-free development.

Astra Support and Resources

SL1680 System Block Diagram

Resources

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