Software & Middleware Optimization

Unlocking maximum performance through intelligent software optimizations that complement our ASIC hardware

The Complete AI Stack

How software, middleware, and hardware work together

Hardware Layer

ASIC chips optimized for matrix operations and AI workloads

  • • Specialized AI accelerators
  • • High-bandwidth memory
  • • Energy-efficient design

Middleware Layer

Intelligent optimization layer that bridges hardware and software

  • • Graph fusion & optimization
  • • Parallel execution management
  • • Memory allocation optimization

Software Layer

Model compression and optimization techniques

  • • Model pruning & quantization
  • • Framework optimizations
  • • Runtime performance tuning

Synergistic Performance

Our servers leverage optimizations at every layer to deliver up to 200x performance improvements over traditional CPU-based systems, with 80% lower energy consumption.

Model Compression Techniques

Making AI models faster and more efficient without sacrificing accuracy

Neural Network Pruning

Removing unnecessary connections (weights) from neural networks without significantly impacting accuracy.

How It Works

  • • Identify low-magnitude weights
  • • Remove connections below threshold
  • • Fine-tune remaining network
  • • Maintain accuracy while reducing size
90%
Size Reduction
<1%
Accuracy Loss

Before vs After Pruning

Original Network
Dense connections
Pruned Network
Sparse connections

Quantization

Reducing the precision of model weights from 32-bit floating point to 8-bit or even 4-bit integers.

Precision Levels

FP32 (Training) 32 bits
FP16 (Half Precision) 16 bits
INT8 (Quantized) 8 bits
INT4 (Ultra-Low) 4 bits
4x
Speed Increase
75%
Memory Savings

Memory Usage Comparison

FP32 150GB
FP16 75GB
INT8 37.5GB
INT4 18.75GB
Example: 175B parameter model (like GPT-3)

Middleware Optimization

Intelligent orchestration layer that maximizes hardware utilization

Graph Fusion

Combining multiple neural network operations into single, optimized kernels to reduce memory transfers and latency.

Optimization Strategy

  • • Merge consecutive operations
  • • Eliminate intermediate memory writes
  • • Reduce CPU-GPU communication
  • • Optimize memory access patterns
5x
Fewer Memory Transfers
40%
Latency Reduction

Parallel Execution

Smart distribution of computational graphs across multiple ASICs and GPUs for maximum throughput.

Distribution Strategy

  • • Split large models across devices
  • • Pipeline parallel processing
  • • Dynamic load balancing
  • • Minimize inter-device communication

Example: 175B Model Distribution

Single GPU (A100) Won't fit (80GB memory)
Our Solution (4 ASICs) Fits + 3x faster

Combined Middleware Impact

50%
Lower Latency
3x
Higher Throughput
60%
Better Resource Utilization
40%
Energy Savings

Real-World Performance Gains

Measured improvements from our software + hardware optimization stack

Large Language Models

Model: GPT-3 Scale (175B params)
Baseline (GPU): 2.1 sec/query
With Quantization: 0.8 sec/query
+ Graph Fusion: 0.4 sec/query
+ Our ASICs: 0.15 sec/query
14x
Performance Improvement

Computer Vision

Model: ResNet-50 Image Classification
Baseline (CPU): 500ms/image
With Pruning: 200ms/image
+ Quantization: 80ms/image
+ Our ASICs: 15ms/image
33x
Performance Improvement

Recommendation Systems

Deep Learning Recommendation Model
Baseline: 1K req/sec
With Optimization: 5K req/sec
+ Middleware: 12K req/sec
+ Our ASICs: 25K req/sec
25x
Throughput Increase

Get the Complete Optimization Stack

Hardware + Software + Middleware optimizations working together to deliver unprecedented AI performance.

Explore Optimized Servers Talk to Our Engineers