Researchers Accelerated NTT Computations on AI Hardware
A new loop-based optimization method significantly boosts polynomial operations required for post-quantum cryptography.
Updated on Oct. 14, 2026 in Quantum Computing
Researchers have demonstrated a methodology for accelerating number theoretic transform (NTT) operations on neural processing units. The team achieved measurable performance gains by deploying NTT implementations across the AMD AI Engine architecture.
Why it matters
Efficient polynomial multiplication is a prerequisite for lattice-based post-quantum cryptography and fully homomorphic encryption. Neural processing units have remained largely unexplored for these cryptographic workloads compared to traditional GPU and FPGA platforms.
The researchers implemented NTT configurations using radix-2, radix-4, and radix-8 layouts. These support polynomial sizes up to degree 4096 for 14-bit moduli and degree 2048 for 30-bit moduli.
The players
AMD
A semiconductor company that produces high-performance computing processors and AI Engine hardware.
The details
The methodology employs manual loop fission—a compiler optimization technique that splits large, complex loops into smaller, independent primitives—to enable software pipelining. By constructing specific Barrett reduction primitives, which are specialized mathematical operations for modular reduction, the team built high-throughput NTT implementations tailored for the AMD AI Engine's tile-based architecture.
Timeline
The research methodology for NTT acceleration was published on October 14, 2026.
The Tech Race
Lattice-based post-quantum cryptography requires massive polynomial arithmetic that typically strains general-purpose CPUs. This research marks a shift in the race to optimize these primitives by successfully offloading core operations to neural processing units rather than traditional graphics hardware.
This research demonstrates how existing AI hardware can be repurposed to accelerate essential post-quantum encryption tasks. Developers and security engineers can monitor these findings to see if neural processors become a viable alternative to GPUs for cryptographic acceleration.
The takeaway
The research proves that neural processing units can effectively handle high-throughput cryptographic workloads if optimized with loop fission techniques. Readers should watch for future hardware-specific optimizations of NIST-approved post-quantum algorithms on similar NPU-based architectures.
Further reading
Explore more developments in the field of Quantum Computing.
