CNN
Hardware acceleration target for convolutional neural network workloads.
An embedded FPGA IP that replaces generic DSP cells with Pedestal AI DSP cells for hardware acceleration targeting CNN, LLM, and VLM.
The FPGA fabric retains standard programmable logic, memory, routing, and I/O resources, while conventional DSP blocks are replaced with Pedestal AI DSP blocks optimized for AI acceleration.
Traditional embedded FPGA architecture combines programmable logic, memory, routing, I/O, and conventional DSP resources.
Pedestal retains the standard FPGA fabric while replacing conventional DSP resources with AI-optimized Pedestal AI DSP blocks.
The Embedded FPGA-Based AI Accelerator IP is associated with three currently provided performance points.
Contact Pedestal for implementation-specific product information.
Pedestal AI DSP cells target three confirmed AI workload categories within the embedded FPGA architecture.
Hardware acceleration target for convolutional neural network workloads.
Hardware acceleration target for large language model workloads.
Hardware acceleration target for vision-language model workloads.
Pedestal's established NPU IP positioning remains central. The Embedded FPGA-Based AI Accelerator IP adds an embedded FPGA capability alongside Pedestal's existing AI acceleration, DSP, and ASIC/SoC-related capabilities.
Contact Pedestal for product and implementation-specific information.
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