5 papers
TimingLLM: A Two-Stage Retrieval-Augmented Framework for Pre-Synthesis Timing Prediction from Verilog
Armin Abdollahi, Negin Ashrafi, Mehdi Kamal +1
Early, tool-free prediction of post-synthesis timing remains a key obstacle to rapid RTL iteration. We introduce TimingLLM, a two-stage retrieval-augmented LLM pipeline that estima…
HDLFORGE: A Two-Stage Multi-Agent Framework for Efficient Verilog Code Generation with Adaptive Model Escalation
Armin Abdollahi, Saeid Shokoufa, Negin Ashrafi +2
We present HDLFORGE, a two-stage multi-agent framework for automated Verilog generation that optimizes the trade-off between generation speed and accuracy. The system uses a compac…
RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts
Armin Abdollahi, Mehdi Kamal, Massoud Pedram
This paper presents RocketPPA, a novel ultra-fast power, performance (delay), and area (PPA) estimator operating directly at the code-level abstraction using HDL code as input. The…
IC-D2S: A Hybrid Ising-Classical-Machines Data-Driven QUBO Solver Method
Armin Abdollahi, Mehdi Kamal, Massoud Pedram
We present a heuristic algorithm designed to solve Quadratic Unconstrained Binary Optimization (QUBO) problems efficiently. The algorithm, referred to as IC-D2S, leverages a hybrid…
MENAGE: Mixed-Signal Event-Driven Neuromorphic Accelerator for Edge Applications
Armin Abdollahi, Mehdi Kamal, Massoud Pedram
This paper presents a mixed-signal neuromorphic accelerator architecture designed for accelerating inference with event-based neural network models. This fully CMOS-compatible acce…