Andrew Hennessee | Searcharxiv

arXiv2025

A Spatial Array for Spectrally Agile Wireless Processing

Massive MIMO is a cornerstone of next-generation wireless communication, offering significant gains in capacity, reliability, and energy efficiency. However, to meet emerging demands such as high-frequency operation, wide bandwidths, co-existence, integrated sensing, and resilience to dynamic interference, future systems must exhibit both scalability and spectral agility. These requirements place increasing pressure on the underlying processing hardware to be both efficient and reconfigurable. This paper proposes a custom-designed spatial array architecture that serves as a reconfigurable, general-purpose core optimized for a class of wireless kernels that commonly arise in diverse communications and sensing tasks. The proposed spatial array is evaluated against specialized cores for each kernel using High-Level Synthesis (HLS). Both the reconfigurable and specialized designs are synthesized in a 32 nm process to assess latency, throughput, area, and power in realistic processes. The results identify conditions under which general-purpose systolic architectures can approach the efficiency of specialized cores, thereby paving the way toward more scalable and agile systems.

eess.SP↗

arXiv2025

Can Reasoning Models Reason about Hardware? An Agentic HLS Perspective

Recent Large Language Models (LLMs) such as OpenAI o3-mini and DeepSeek-R1 use enhanced reasoning through Chain-of-Thought (CoT). Their potential in hardware design, which relies on expert-driven iterative optimization, remains unexplored. This paper investigates whether reasoning LLMs can address challenges in High-Level Synthesis (HLS) design space exploration and optimization. During HLS, engineers manually define pragmas/directives to balance performance and resource constraints. We propose an LLM-based optimization agentic framework that automatically restructures code, inserts pragmas, and identifies optimal design points via feedback from HLs tools and access to integer-linear programming (ILP) solvers. Experiments compare reasoning models against conventional LLMs on benchmarks using success rate, efficiency, and design quality (area/latency) metrics, and provide the first-ever glimpse into the CoTs produced by a powerful open-source reasoning model like DeepSeek-R1.

cs.AI↗