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Benefits

  • Cross-Scale Flexibility – From single-car AI chips to hyperscale clusters.
  • Realistic Workload Modeling – Supports tensor math, buffers, and AI traffic.
  • Performance Validation – Evaluate latency, throughput, and bottlenecks early.
  • Energy Efficiency – Optimize TPU configurations for power-sensitive designs.
  • System-Level Insight – See how TPUs interact with memory, interconnects, and storage.

The TPU (Tensor Processing Unit) model in VisualSim is designed to simulate the performance and behavior of specialized AI accelerators. It supports both standalone TPUs (e.g., Google) and integrated tensor cores inside GPUs (e.g., NVIDIA, AMD).

By replicating the parallel matrix computation capabilities, along with on-chip buffers, external DDR memory, and optimized dataflow architectures, the TPU block provides an accurate way to explore AI/ML workloads at scale.

VisualSim allows designers to evaluate how TPUs behave when deployed as:

  • 1–2 units in an edge device or autonomous car.
  • 1000s of units in a cloud-scale AI training cluster.

Key Parameters

  • n_value – Parallelism factor for tensor ops.
  • External_Mem – DDR/HBM configurations.
  • On_Chip_Mem – Unified buffer size.
  • TPU_Speed_MHz – Core frequency.

Parameter Snapshots

  • Parameter

Simulation Results

  • Performance Scaling Studies – From 1 TPU to 1000s.
  • Memory Access Latency – On-chip vs external DRAM/HBM.
  • Energy Efficiency Reports – Power per inference/training cycle.
  • System Bottleneck Analysis – TPU communication over interconnects.
  • Plot

Application

  • Edge AI – Inference in IoT and automotive.
  • Autonomous Systems – Cars, drones, and robotics with real-time AI workloads.
  • Cloud & Data Centers – TPU racks for large-scale AI training and inference.
  • Embedded AI – ML workloads in consumer electronics and industrial automation.
  • AI Infrastructure Exploration – Design sizing for scaling from a handful of TPUs to thousands.

Integrations

  • Works with NoCs, PCIe, Ethernet, NVMe, and DDR/HBM memory models.
  • Co-simulates with RISC-V, ARM, and GPU models for heterogeneous architectures.
  • Integrates with Task Graphs to drive AI/ML workloads across pipelines.

View Demo

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