Maia 200 AI Accelerator System

# Illustrative sketch (not from the paper)
import torch

# Placeholder for Maia 200 SDLA runtime API
class Maia200:
    def __init__(self):
        # Initialize separate control and data paths
        self.control = SDLAControl()
        self.data = SDLADatapath()
        # Attach locally accessed scratchpad memories
        self.csram = CSRAM()
        self.tsr = TSRAM()
        # Network controller for 1D Hamming Mesh
        self.net = ANC()

    def load_model(self, model_path):
        # Load model weights directly into CSRAM for low‑latency access
        self.csram.load_weights(model_path)

    def infer(self, input_tensor):
        # Asynchronously launch data movement and compute via Dataflow ISA
        self.control.dispatch_compute(input_tensor, self.csram, self.tsr)
        return self.control.wait_result()

# Mock components (no real implementation)
class SDLAControl:    
    def dispatch_compute(self, inp, csram, tsr):
        # Program dataflow engine to move inputs to TS RAM, compute, write back
        pass
    def wait_result(self):
        # Retrieve result from CSRAM after execution
        return torch.tensor([0])

class SDLADatapath:    
    pass
class CSRAM:
    def load_weights(self, path):
        pass
class TSRAM:
    pass
class ANC:
    def send(self, data):
        pass

# Example usage with Qwen 2.5 7B (public model)
maia = Maia200()
maia.load_model("qwen2.5_7b_weights.bin")
# Dummy input tensor representing token IDs
input_ids = torch.randint(0, 32000, (1, 128))
output = maia.infer(input_ids)
print("Inference result placeholder:", output)

// Illustrative sketch (not from the paper)
const torch = require('torch-js'); // placeholder for PyTorch-like API

// Mock Maia 200 SDLA runtime objects
class Maia200 {
  constructor() {
    // Separate control and data paths
    this.control = new SDLAControl();
    this.data = new SDLADatapath();
    // Locally accessed scratchpad memories
    this.csram = new CSRAM();
    this.tsr = new TSRAM();
    // Network controller for 1D Hamming Mesh topology
    this.net = new ANC();
  }

  loadModel(modelPath) {
    // Load weights directly into CSRAM
    this.csram.loadWeights(modelPath);
  }

  async infer(inputTensor) {
    // Dispatch asynchronous data movement and compute via Dataflow ISA
    this.control.dispatchCompute(inputTensor, this.csram, this.tsr);
    return await this.control.waitResult();
  }
}

// Mock component classes (no real functionality)
class SDLAControl {
  dispatchCompute(inp, csram, tsr) {
    // Program dataflow engine; placeholder
  }
  async waitResult() {
    // Return placeholder tensor after execution
    return torch.tensor([0]);
  }
}
class SDLADatapath {}
class CSRAM { loadWeights(path) {} }
class TSRAM {}
class ANC { send(data) {} }

// Example usage with Qwen 2.5 7B model
(async () => {
  const maia = new Maia200();
  maia.loadModel('qwen2.5_7b_weights.bin');
  // Dummy input representing token IDs
  const inputIds = torch.randint(0, 32000, [1, 128]);
  const output = await maia.infer(inputIds);
  console.log('Inference result placeholder:', output);
})();