RTL Generation
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
CASS-RTL: Correctness-Aware Subspace Steering for RTL Generation with LLMs
arXiv:2606.05680v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have enabled the automatic synthesis (generation) of register-transfer level (RTL) code from natural language instructions, offering a promising pathway to accelerate chip design. Unlike typical natural language (and software coding) tasks, LLM-based RTL code generation demands strict cycle accuracy with concurrency, where minor logical errors can render a circuit unusable or insecure.
RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models
arXiv:2606.08976v1 Announce Type: new Abstract: LLM-based RTL generation and reasoning is a promising direction for hardware design automation. High-quality benchmarks are critical infrastructure for tracking progress in this direction. However, existing RTL benchmarks face inherent limitations in both scale and task scope.
StepPRM-RTL: Stepwise Process-Reward Guided LLM Fine-Tuning for Enhanced RTL Synthesis
Announce Type: new Abstract: Automatic generation of RTL code for digital hardware designs remains challenging due to long-horizon reasoning, multi-step dependencies, and strict correctness constraints in Verilog and VHDL. We present StepPRM-RTL, a novel framework that combines stepwise trajectory modeling, process-reward modeling (PRM), and retrieval-augmented fine-tuning (RAFT) to enhance both the functional correctness and reasoning fidelity of LLM-based RTL code generation. StepPRM-RTL...
LongRTL: Graph-Similarity-Guided LLM-driven Long Context RTL Optimization
Announce Type: new Abstract: Large Language Models (LLMs) show great promise in RTL code generation and optimization. However, real-world RTL designs are typically long, entangled, and poorly modularized, posing a major challenge due to context-length limitations and lack of structure. To overcome these obstacles, we propose a scalable LLM-based RTL optimization framework guided by graph similarity.
Alpha-RTL: Test-Time Training for RTL Hardware Optimization
arXiv:2606.05253v1 Announce Type: new Abstract: Large language models (LLMs) have shown increasing promise in generating functionally correct register-transfer-level (RTL) hardware designs. Recent systems improve further through EDA-integrated reinforcement learning with syntax, simulation, and PPA rewards, but train a general RTL generator before deployment while test-time approaches search with a frozen policy. We instead perform reinforcement learning at test time, allowing the LLM policy...
GoldenFloat: A Phi-Derived Static-Split Floating-Point Family from GF4 to GF256 with a Lucas-Exact Integer Identity
arXiv:2606.05017v1 Announce Type: new Abstract: We present a hardware-oriented description of GoldenFloat (GF), a static-split floating-point family generated by a single closed rule, and three concrete artefacts: (i) an open multi-width RTL generator covering GF4-GF256 with a continuous-integration differential sweep against a correctly-rounded reference; (ii) an integer-backed Lucas-exact accumulator path verified at 500-digit precision for n = 1, ..., 256; and (iii) a GF16 FPGA codec...
LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges
arXiv:2605.10807v4 Announce Type: replace Abstract: The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry. While LLMs offer unprecedented capabilities in generating Register Transfer Level (RTL) code, automating testbenches, and bridging the semantic gap between high-level specifications and silicon, they simultaneously introduce severe vulnerabilities. This comprehensive review provides...
Programming Domain-Specific FPGA Hardblocks from HLS: An RTL Blackbox Approach
Announce Type: new Abstract: Domain-specific Field Programmable Gate Array (FPGA) architectures increasingly integrate specialized hardblocks, such as Tensor Slices, to accelerate artificial intelligence and machine learning workloads. Despite their efficiency benefits, these architectures remain difficult to program because designers typically rely on manual Register-Transfer Level (RTL) integration to access these hardblocks. This paper presents a compiler-agnostic methodology that enables...
PALUTE: Processing-In-Memory Acceleration via Lookup Table for Edge LLM Inference
arXiv:2606.08891v1 Announce Type: new Abstract: Large language models are increasingly deployed on edge devices with tight power and area budgets. While mixed-precision GEMM reduces arithmetic complexity, quantized inference is often dominated by dequantization and nonlinear operators. Lookup Table (LUT)-based method mitigates these costs by precomputing outputs and replacing repeated arithmetic with table lookups, but existing designs incur significant capacity and lookup-latency overheads.
Germany upbeat as final World Cup preparations begin
Germany upbeat as final World Cup preparations begin June 9, 2026"We have excellent conditions here, both on the pitch and off it," Germany coach Julian Nagelsman told reporters a few hours after the team's arrival at Wake Forest University campus in Winston-Salem, USA. "If you feel comfortable and can prepare well, the likelihood of playing good matches increases. And if you deliver good performances, the chances of success rise, too," added the 38-year-old, who is about to take part in his...