Supporting Your Artificial Intelligence SoC Architecture Requirements

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Specialized Processing

Processors to manage massive and changing compute requirements for machine and deep learning tasks. 

  • Neural processor IP supports latest high-performance neural network models, including transformer, CNN & RNN based models
  • Efficient ARC Processors provide scalar and vector capabilities and customizable APEX accelerators; tightly coupled memories alleviate bottlenecks; ARC Subsystems enable key AI functions
  • ASIP Designer supports custom processing with parallelism and specialized datapath elements
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Memory Performance

IP solutions to support efficient architectures for different memory constraints including bandwidth, capacity, cache coherency. 

  • DDR IP addresses capacity needs 
  • HBM2 IP addresses the bandwidth bottleneck 
  • CCIX enables cache coherency with virtualized memory capabilities for heterogeneous computing and reduced latency
  • Foundation IP provides a wide array of embedded memories, logic libraries, TCAMs, and multi-port memories, with high density, low leakage, and high performance options 
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Real-Time Data Connectivity

Synopsys provides reliable connectivity to CMOS image sensors, microphones, and motion sensors for AI applications including vision, natural language understanding, and context awareness.

  • IP in advanced FinFET technologies reduce power
  • Broad portfolio of silicon-proven IP support the most widely used interfaces such as MIPI, USB/Display Port, HDMI, PCI Express, CCIX, Ethernet, etc.

Watch Video: Synopsys IP for Amazing AI SoCs

Recent innovations in deep learning algorithms and neural network processing is driving new technology requirements for artificial intelligence (AI) SoCs. Deep learning capabilities are being added to SoCs across all markets including mobile, IoT, data center, automotive, and digital home. Synopsys’ silicon-proven IP portfolio is addressing the diverse processing, memory, and connectivity requirements of each market.

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