Hardware Acceleration for FHE:
GPUs, FPGAs, and ASICs

FHE's computational intensity makes it a prime candidate for hardware acceleration. GPUs, FPGAs, and custom ASICs can speed up FHE by orders of magnitude, making previously impractical applications feasible.

Why Hardware Acceleration?

FHE workloads are characterized by:

• Large polynomial operations
• Number Theoretic Transforms (NTT)
• Massive parallelism potential
• Memory-intensive operations

These characteristics map well to specialized hardware.

CPU Optimizations

Before jumping to accelerators, maximize CPU performance:

Modern CPUs with AVX-512 significantly accelerate FHE compared to baseline.

GPU Acceleration

GPUs excel at parallel polynomial operations:

Advantages:

• Massive parallelism (thousands of cores)
• High memory bandwidth
• Widely available hardware
• Existing CUDA/OpenCL expertise

Considerations:

• Memory transfer overhead
• Not all FHE operations parallelize equally
• Power consumption

// Conceptual GPU FHE kernel
__global__ void ntt_kernel(uint64_t* data, uint64_t* twiddles, int n) {
  int idx = blockIdx.x * blockDim.x + threadIdx.x;
  // Parallel butterfly operations
  // Each thread handles one coefficient
}

GPU implementations achieve 10-100x speedups for suitable workloads.

FPGA Acceleration

FPGAs offer customizable hardware:

Advantages:

• Custom datapaths optimized for FHE
• Lower latency than GPU
• Energy efficient
• Reconfigurable for different schemes

Considerations:

• Development complexity
• Limited memory
• Longer development cycles

Microsoft's FPGA-accelerated CKKS demonstrates 100x+ improvements.

ASIC Development

Custom ASICs represent the ultimate acceleration:

Advantages:

• Maximum performance
• Optimal energy efficiency
• Dedicated FHE architecture

Considerations:

• Very high development cost
• Long development timeline
• Inflexible once manufactured

Several startups are developing FHE ASICs claiming 10,000x speedups.

Acceleration Strategy

Choose acceleration based on your needs:

• Development/Testing: CPU with AVX-512
• Production (flexible): GPU acceleration
• Production (specialized): FPGA or cloud FHE services
• High-volume production: Consider ASIC investment

Cloud FHE Services

Cloud providers are offering accelerated FHE:

• AWS, Azure, GCP experimenting with FHE offerings
• Specialized FHE cloud services emerging
• Managed acceleration without hardware investment

H33's Approach

We use a combination of:

• Highly optimized CPU implementations with AVX-512
• Custom algorithmic optimizations for biometric workloads
• Hardware acceleration for high-volume operations

This achieves our 1.28ms Full Stack Auth performance.

Hardware acceleration is transforming FHE from academic curiosity to production technology. The trend toward specialized FHE hardware will only accelerate.