Low-Stack HAETAE for Memory-Constrained Microcontrollers
- URL: http://arxiv.org/abs/2604.15868v1
- Date: Fri, 17 Apr 2026 09:18:04 GMT
- Title: Low-Stack HAETAE for Memory-Constrained Microcontrollers
- Abstract summary: We present a low-stack implementation of the module-lattice signature scheme HAETAE.<n>On microcontrollers, peak stack usage is often binding constraint, and HAETAE's hyperball-based sampler poses a particular challenge.<n>We introduce Rejection-aware pass decomposition, which isolates encoding to the post-signature path path.<n>We reduce HAETAE-2, -3, and -5 stack by respectively 75, 86 and 8 % for key generation, 92, 95 and 24 % for signature generation, and 85, 91 and 22 % for verification.
- Score: 0.1749935196721634
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a low-stack implementation of the module-lattice signature scheme HAETAE, targeting microcontrollers with 8 kB-16 kB of available SRAM. On such devices, peak stack usage is often the binding constraint, and HAETAE's hyperball-based sampler, large transient polynomial vectors, and variable-length signature payloads (hint and high-bits arrays) pose a particular challenge. To address this we introduce (i) Rejection-aware pass decomposition, which isolates encoding to the post-acceptance path; (ii) Component-level early rejection, which short-circuits the response computation when a partial norm already exceeds the bound; and (iii) Reverse-order streaming entropy coding using range Asymmetric Numeral Systems (rANS), which eliminates full hint and high-bits staging buffers. Combined with streamed matrix generation, a two-pass hyperball sampler with streaming Gaussian backend, and row-streamed verification, these techniques bring Signing stack from 71 kB-141 kB in the reference implementation down to 5.8 kB-6.0 kB, key generation to 4.7 kB-5.7 kB, and verification to 4.7 kB-4.8 kB across all three security levels. Our pure C implementation covers all three security levels (HAETAE-2/3/5), whose optimization paths differ due to the public-key domain (d>0 vs. d=0) and rejection structure. We implement our optimization on a Nucleo-L4R5ZI and compare to the reference pqm4 (for HAETAE-2 and -3) and a recently published memory-optimized implementation (targeting HAETAE-5 only). We reduce HAETAE-2, -3, and -5 stack by respectively 75, 86 and 8 % for key generation, 92, 95 and 24 % for signature generation, and 85, 91 and 22 % for verification. Depending on the parameter set, this impacts performance by at most a factor 1.8 and 3.4 for key and signature generation respectively, while even offering a performance improvement up to 18 % for verification. Verification at all security levels fits within 8 kB of RAM (signature buffer + stack) and is 2.34-3.34x faster than ML-DSA m4fstack at each comparable security level. We additionally validate portability under RIOT-OS on ARM Cortex-M4 and RISC-V targets.
Related papers
- Why Gated DeltaNet Survives 4-Bit Quantization: NVFP4 W4A4 for the Recurrent Half of a Hybrid 27B LLM [0.0]
Minima: NVFP4 W4A4 on all 496 linear layers, GDN included.<n>A four-part mechanism study explains why NVFP4's 16-element block scaling localizes the residual stream's extreme outliers.<n>A mechanistic account of why the recurrent half of a hybrid LLM is the easy half to quantize.
arXiv Detail & Related papers (2026-09-03T17:04:26Z) - On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability [57.03393882933515]
We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per token, and 51B parameters of n-gram embedding tables held off the accelerator.<n>On fourteen pre-training benchmarks the model leads the 397B-A17B predecessor on eight and trails it on the rest by at most 2.6 points, at 1/3 the activated parameters, 1/3 the training tokens, and roughly 1/9 the training FLOPs.
arXiv Detail & Related papers (2026-08-31T06:35:07Z) - From Bit-Position Sensitivity to Unequal Error Protection for DNN Inference Memory [0.4610618579893946]
We characterize per-bit-position fault sensitivity in ML inference across 16 workloads.<n>Our central empirical finding is a sharp bit-sensitivity transition: flipping any of the least-significant fraction bits up to a data-type-specific threshold, Xsafe, degrades task metrics by less than 1% under deterministic single-bit stress tests.
arXiv Detail & Related papers (2026-07-21T23:07:56Z) - ChipCraftBrain: Validation-First RTL Generation via Multi-Agent Orchestration [0.0]
Multi-agent approaches such as MAGE reach 95.9% on VerilogEval yet remain untested on harder industrial benchmarks.<n>We present ChipCraftBrain, a framework combining symbolic-neural reasoning with adaptive multi-agent orchestration for automated RTL generation.
arXiv Detail & Related papers (2026-04-21T17:20:24Z) - Scaling DoRA: High-Rank Adaptation via Factored Norms and Fused Kernels [83.99688944263843]
Weight-De Low-Rank Adaptation (DoRA) extends LoRA by decoupling weight magnitude from direction.<n>At d_in = 8192 and rank r = 384, a single module's norm requires about 512 MB of transient working memory in bf16.<n>A factored norm decomposes the squared norm into base, cross, and Gram terms computable through O(d_out r + r2) intermediates, eliminating the dense product.
arXiv Detail & Related papers (2026-03-23T17:57:24Z) - Benchmarking Post-Quantum Cryptography on Resource-Constrained IoT Devices: ML-KEM and ML-DSA on ARM Cortex-M0+ [0.0]
This paper presents the first isolated algorithm-level benchmarks of ML-KEM and ML-DSA on ARM Cortex-M0+ processors.<n>Using PQClean reference C implementations, we measure all three security levels of ML-KEM across key generation, encapsulation/signing, and decapsulation/verification.<n>All code, data, and scripts are released as an open-source benchmark suite for developers.
arXiv Detail & Related papers (2026-03-19T11:27:29Z) - Architecture-Aware LLM Inference Optimization on AMD Instinct GPUs: A Comprehensive Benchmark and Deployment Study [0.0]
Cross-architecture evaluation of production LLM inference on AMD Instinct MI325X GPUs.<n> benchmarking four models spanning 235B to 1 trillion parameters across three architectural families.
arXiv Detail & Related papers (2026-02-27T13:21:48Z) - BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models [56.504879072674015]
We propose Bit-Plane Decomposition Quantization (BPDQ), which constructs a variable quantization grid via bit-planes and scalar coefficients.<n>BPDQ enables serving Qwen2.5-72B on a single GTX 3090 with 83.85% GSM8K accuracy (vs. 90.83% at 16-bit)
arXiv Detail & Related papers (2026-02-04T02:54:37Z) - Next Block Prediction: Video Generation via Semi-Autoregressive Modeling [92.60177942930946]
Next-Block Prediction (NBP) is a semi-autoregressive (semi-AR) framework for video generation.<n>NBP employs bidirectional attention within each block, enabling tokens to capture more robust spatial dependencies.<n>Our model achieves FVD scores of 103.3 on UCF101 and 25.5 on K600, outperforming the vanilla NTP model by an average of 4.4.
arXiv Detail & Related papers (2025-02-11T17:57:53Z) - Breaking the Memory Barrier: Near Infinite Batch Size Scaling for Contrastive Loss [59.835032408496545]
We propose a tile-based strategy that partitions the contrastive loss calculation into arbitrary small blocks.
We also introduce a multi-level tiling strategy to leverage the hierarchical structure of distributed systems.
Compared to SOTA memory-efficient solutions, it achieves a two-order-of-magnitude reduction in memory while maintaining comparable speed.
arXiv Detail & Related papers (2024-10-22T17:59:30Z) - Optimizing the Deployment of Tiny Transformers on Low-Power MCUs [12.905978154498499]
This work aims to enable and optimize the flexible, multi-platform deployment of encoder Tiny Transformers on commercial MCUs.
Our framework provides an optimized library of kernels to maximize data reuse and avoid data marshaling operations into the crucial attention block.
We show that our MHSA depth-first tiling scheme reduces the memory peak by up to 6.19x, while the fused-weight attention can reduce the runtime by 1.53x, and number of parameters by 25%.
arXiv Detail & Related papers (2024-04-03T14:14:08Z) - The case for 4-bit precision: k-bit Inference Scaling Laws [75.4335600212427]
Quantization methods reduce the number of bits required to represent each parameter in a model.
The final model size depends on both the number of parameters of the original model and the rate of compression.
We run more than 35,000 zero-shot experiments with 16-bit inputs and k-bit parameters to examine which quantization methods improve scaling for 3 to 8-bit precision.
arXiv Detail & Related papers (2022-12-19T18:48:33Z) - A TinyML Platform for On-Device Continual Learning with Quantized Latent
Replays [66.62377866022221]
Latent Replay-based Continual Learning (CL) techniques enable online, serverless adaptation in principle.
We introduce a HW/SW platform for end-to-end CL based on a 10-core FP32-enabled parallel ultra-low-power processor.
Our results show that by combining these techniques, continual learning can be achieved in practice using less than 64MB of memory.
arXiv Detail & Related papers (2021-10-20T11:01:23Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.