playing with TPUs

 Yes — you can experiment with and simulate TPUs, but the options depend on what exactly you want to learn (hardware architecture vs. running ML workloads) and your access level.

Here’s a breakdown that’s useful for an EE student:

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🧠 1) Google’s Official TPU Access (Cloud TPUs)

Google doesn’t provide a built-in interactive hardware simulator for its full TPUs like you might have for a CPU or GPU in an emulator — but you can run real TPU workloads on Google Cloud:

• Cloud TPUs let you provision real TPU hardware on Google Cloud and run TensorFlow, JAX, or PyTorch workloads on them. TensorFlow

• You can use TPUs for training or inference on real models, which gives you practical experience with how TPUs behave in ML workloads. TensorFlow

Tip for students: If you can get credits or a billing account, Google Cloud often has free credits for students (via GitHub Student Pack or Google Cloud for Education). You can then spin up TPUs and play with them, though this is hardware/VM cost-based, not a simulator.

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🧪 2) Educational Notebooks / Colab TPU Access

You can experiment with TPUs inside notebooks:

• Some Google Colab notebooks let you request a TPU runtime to run TensorFlow or PyTorch code. This is a practical way to learn how TPU execution and distributed strategies work. TensorFlow

However:

• Availability of TPUs (and which version) on Colab Free/Pro varies, and sometimes older versions are retired. Reddit

This isn’t a simulator in the hardware sense, but you can run real TPU code without provisioning Cloud resources yourself.

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🛠️ 3) Open-Source TPU “Simulators” or Reimplementations

While Google doesn’t release a cycle-accurate TPU simulator, there are community/open-source projects that try to emulate TPU semantics for educational or research purposes:

🔹 OpenTPU

• An open-source reimplementation of a TPU-like design (functional/hardware simulation). GitHub

• It’s not Google’s official spec (Google doesn’t publish the ISA), but it gives you something you can run, inspect, and simulate the core architecture using Python/PyRTL. GitHub

• Great for EE/hardware systems coursework or understanding how systolic arrays and TPU pipelines can work.

👉 Good for architectural experimentation and simulation.

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📚 4) Emulator-Like Tools for Related Processors

Google’s Coral Edge TPU (a smaller TPU meant for edge/embedded use) does have simulators available through Google’s developer tools, but that’s a different chip from Cloud TPUs:

• Google provides instruction-level simulators for Coral Edge TPUs for testing software without hardware. Google for Developers

👉 Not full Cloud TPU hardware — but good for software stack learning or compiler experimentation.

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📘 What Google Doesn’t Provide

• There’s no official public cycle-accurate simulator of the full Cloud TPU architecture available from Google.

• TPUs are proprietary ASICs, and detailed internal specs aren’t open the way CPU/GPU ISAs are.

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🧠 If You Want to Learn Architecture

Here are practical options based on what you want:

Goal	Best Approach
Hardware/ISA understanding	Use OpenTPU or build your own simulator in PyRTL / Verilog. GitHub
ML workload execution on TPUs	Use Cloud TPU in GCP or TPU runtime in Colab. TensorFlow
Embedded/edge TPU experimentation	Try Coral Edge TPU simulator tools from Google. Google for Developers

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🚀 Learning Resources

• TensorFlow TPU guide — examples of writing TPU-ready code. TensorFlow

• OpenTPU on GitHub — simulatable TPU-like hardware. GitHub

• Educational YouTube/Colab tutorials on TPUs (concepts and demos). classcentral.com

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If you tell me whether you’re more interested in hardware architecture (design/simulation) vs. software/ML training on TPUs, I can tailor a step-by-step plan (including code/notebook examples) for your project!

I want to be able to teach say what happens if you pipeline and try out which sections are better, more of advcance vlsi architectures class

Great question — for an advanced VLSI / computer architecture class, what you’re really after is architectural exploration, not “running TensorFlow on a TPU”. And you’re right: TPUs are a perfect teaching vehicle for pipelining, dataflow, and throughput-oriented design.

Short answer:
Google does not provide a pedagogical TPU pipeline simulator, but you can absolutely teach and experiment with TPU-like architectures using well-established academic tools and even build a clean, modular “TPU-style” simulator that’s better for teaching than Google’s real hardware.

Below is a teaching-oriented roadmap that many architecture courses use (implicitly or explicitly).

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1️⃣ What You Should Teach (TPU as an Architectural Case Study)

Instead of “TPU” as a black box, frame it as:

A deeply pipelined, systolic, dataflow accelerator for dense linear algebra

Core concepts you can explore quantitatively:

• Pipeline depth vs frequency

• Initiation interval (II) vs latency

• Systolic array timing

• Weight-stationary vs output-stationary dataflows

• On-chip buffer sizing vs off-chip bandwidth

• Pipeline bubbles and back-pressure

• Arithmetic intensity vs memory bottlenecks

This maps perfectly to an advanced VLSI course.

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2️⃣ The Right Kind of “TPU Simulator” for Teaching

Instead of a vendor simulator, use parameterized architectural simulators where students can change pipeline structure and measure throughput.

✅ Best-fit tools for your goal

1. PyRTL (Highly recommended for teaching)

• Python-based RTL

• Easy to model:

  • Pipeline registers

  • Multi-stage MAC units

  • Valid/ready handshakes

• Cycle-accurate

• Students can:

  • Insert/remove pipeline stages

  • Observe timing, stalls, utilization

💡 This is how several universities teach TPU-like pipelines.

You can build:

• A pipelined MAC

• A 1D systolic array

• Then scale to 2D

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2. gem5 (Architecture-level, not RTL)

Use if you want:

• CPI, throughput, bandwidth studies

• Modeling of accelerators as functional units

But:

• Overkill for pure VLSI pipeline teaching

• Less intuitive for “where exactly do I put the register?”

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3. OpenTPU (Academic TPU-like design)

• Open-source TPU-inspired accelerator

• Uses PyRTL

• Explicit pipeline stages and buffers

• Excellent reference design

This is probably the closest thing to what you want, pedagogically.

You don’t need Google’s TPU — this is better for learning.

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3️⃣ A Concrete Teaching Structure (Very Effective)

Here’s a course module structure that works extremely well.

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Module 1: Baseline MAC Pipeline

Students build:

Input → Multiply → Add → Output

Experiments:

• Single-cycle vs multi-cycle multiply

• Add pipeline registers

• Measure:

  • Latency

  • Throughput

  • Max clock frequency

Key lesson:

Throughput ≠ latency

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Module 2: Pipelined Dot Product Unit

Now chain MACs:

A[i], B[i] → MAC → MAC → MAC → Accumulator

Experiments:

• Fully unrolled vs folded

• Where do pipeline registers help most?

• When does accumulation become the bottleneck?

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Module 3: Systolic Array (TPU Core Idea)

Build a 2D systolic array:

A → [MAC] → [MAC] → ...
      ↓        ↓
     [MAC] → [MAC]

Experiments:

• Pipeline inside MAC vs between MACs

• Weight-stationary vs output-stationary

• Effect of array size on utilization

This is a TPU core.

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Module 4: Memory & Buffering

Add:

• Input FIFOs

• Weight buffers

• Output buffers

Experiments:

• Small vs large buffers

• Back-pressure effects

• Pipeline bubbles due to memory stalls

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Module 5: “What If?” Exploration

Let students answer:

• Where should I pipeline first?

• Is deeper always better?

• What limits throughput now — compute or memory?

This is exactly TPU design thinking.

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4️⃣ Why Google’s Real TPU Is Actually Bad for Teaching This

This is an important insight for students:

Real TPU	Teaching Model
Proprietary	Fully transparent
Fixed pipeline	Parameterized
Black-box timing	Cycle-accurate
ML-centric	Architecture-centric

So you’re not “missing out” by not having Google’s simulator — you’re gaining control.

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5️⃣ If You Want a Minimal Starter Example (Conceptual)

A TPU-style MAC pipeline students might modify:

Stage 1: Load A,B
Stage 2: Multiply
Stage 3: Partial Sum
Stage 4: Accumulate
Stage 5: Writeback

Then ask:

• What happens if Stage 2 is split?

• What if accumulation is unpipelined?

• Where do hazards appear?

This leads naturally into:

• Retiming

• Register balancing

• Throughput analysis

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6️⃣ Bottom Line

For an advanced VLSI architectures class:

• ❌ Google does not provide a teaching-oriented TPU pipeline simulator

• ✅ You should not want one anyway

• ✅ PyRTL + OpenTPU-style designs are ideal

• ✅ You can explicitly teach:

  • Pipeline placement

  • Throughput vs latency

  • Dataflow architectures

  • Systolic array behavior

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If you want, I can:

• Sketch a TPU-style MAC + systolic array assignment

• Provide a PyRTL starter template

• Help you design lab checkpoints & grading metrics

• Or help translate this into Verilog + synthesis timing labs

Just tell me which direction you want to go.

is there any simulator for these kind of architectural exploration. eg how to have a simulator where we can solve mixture of experts configuration for TPU.

Yes — there are architectural simulators and frameworks you can use to explore advanced accelerator design, including things like pipelining strategies, resource allocation, dataflow mapping, and even Mixture-of-Experts (MoE) style partitioning strategies. They’re not full Google TPU simulators from the vendor (those don’t exist publicly), but they let you simulate and explore architectural trade-offs that are highly relevant to TPU-like accelerators. vlsiarch.eecs.harvard.edu+1

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🧪 1) SCALE-Sim — Systolic Array / Neural Accelerator Simulator

What it is:
An open-source cycle-accurate simulator for neural processing units (NPUs) based on systolic array architectures similar to TPU-style MAC arrays. It models timing, memory bandwidth, utilization, and power/energy metrics. scalesim-project.github.io

Why it’s useful for architecture exploration:

• Parameterize array dimensions, buffer sizes, memory bandwidth.

• Study dataflow mapping strategies (weight-stationary, output-stationary, etc.).

• Explore how different workloads (CNNs, transformer layers) affect utilization and stalls.

• Can be extended or integrated into custom workloads if you modify the code.

👉 This is a good simulator for accumulator array choices and pipeline effects across memory hierarchies.

Limitations:

• Focused on DNN accelerators, not general CPU cores.

• Doesn’t natively model dynamic MoE routing logic — but you could modify it to explore resource partitioning for experts.

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🧠 2) Aladdin / gem5-Aladdin — Accelerator Design Space Exploration

What it is:
A pre-RTL hardware accelerator simulator integrated with gem5 that lets you:

• Define high-level accelerator behaviors

• Explore performance and power trade-offs

• Model interactions between accelerators and system memory/cache hierarchies

The Aladdin tool generates a dynamic data dependence graph (DDDG) of your algorithm and evaluates projected performance/power/area, which is useful for early design decisions before RTL. vlsiarch.eecs.harvard.edu+1

Why it’s useful:

• Great for architectural design space search before committing to RTL

• Works well to study communication vs compute bottlenecks

• Good for teaching how accelerators integrate with a host processor

Note: gem5 itself is a widely used architectural simulator that supports plug-in models of custom accelerators and configurations. It’s not trivial to start with but is very powerful for research. Wikipedia

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🏗 3) OpenTPU — PyRTL-Based TPU Reimplementation

What it is:
An open-source reimplementation of a TPU-inspired design in PyRTL (a Python-based hardware description / simulation environment). You can simulate both functional and cycle-accurate behaviors. GitHub

Why it’s useful for teaching:

• Explicit hardware structure you can read and modify

• Good for experimenting with PIPELINE STAGES, systolic dataflows, and parameterizable MAC array

• Lets you add or rearrange pipeline registers

Limitation:

• Based on the original TPU inference architecture, not MoE routing logic

• To explore MoE performance you’d need to extend the design with custom router modules

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🧩 4) Verilog / SystemC Simulators with Personal RTL

If you want full control to build custom pipeline and routing logic:

• Verilator — converts Verilog into fast cycle-accurate C++/SystemC simulation. Great for custom pipeline models you write yourself. Wikipedia

• Commercial EDA simulators (e.g., Cadence NCSim / Xcelium) — full event-driven simulation with waveform debugging, but expensive. Wikipedia

👉 Best for teaching RTL design + performance analysis, but requires you write the RTL.

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📊 5) How to Simulate Mixture of Experts (MoE) Architecture

There’s no off-the-shelf MoE “hardware simulator” specifically for TPUs, but you can approach it from a few angles:

🧠 A) Extend Cycle-Accurate Accelerator Simulators

Take something like SCALE-Sim and:

• Parameterize the execution units to represent MoE experts

• Add a router model that directs tokens/activations to different expert units

• Explore architectural trade-offs (e.g., how many experts per tile, how to assign memory buffers, load balancing)

This aligns with how research groups adapt accelerator simulators to custom workloads.

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🛠 B) Define MoE Behavior in a Simulator Framework

Use:

• gem5 with custom accelerator models (your own expert cores + routing)

• Aladdin / gem5-Aladdin with workload graph definitions

• A custom PyRTL/Verilog accelerator + Verilator simulation

In these, you can model dynamic routing and explore how pipeline depth, expert count, and memory hierarchy affect throughput and latency — the kinds of architectural trade-offs advanced VLSI classes would focus on.

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🔎 Summary of Simulator Tools

Simulator	Main Use	Pipeline/Perf	Custom MoE?
SCALE-Sim	CNN/ML accelerator cycles	✔️	Extendable
gem5 / gem5-Aladdin	System + accelerator research	✔️	Yes (custom models)
Aladdin	Pre-RTL accelerator DSE	✔️	Yes (with custom definition)
OpenTPU (PyRTL)	Teach TPU-like core	✔️	Beginner to extend
Verilator	RTL → fast cycle sim	✔️	Your own RTL
Commercial EDA (e.g., NCSim)	RTL verification	✔️	Your own RTL

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🧠 Practical Recommendation for MoE TPU-Like Exploration

If your goal is architectural exploration (pipeline, expert allocation, routing) for advanced VLSI class:

1. Start with SCALE-Sim or gem5-Aladdin to define and explore different pipeline architectures. scalesim-project.github.io+1

2. Use PyRTL / OpenTPU template to prototype custom pipeline/router design. GitHub

3. Move to cycle-accurate Verilator + custom RTL when you need detailed timing and interaction effects. Wikipedia

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If you want, I can point you to specific GitHub repos and starter templates for these simulators (e.g., SCALE-Sim, gem5-Aladdin, OpenTPU), along with an example of how you might model an MoE router in one of them. Just let me know!