22 citations · 40 across the 25 of their papers we have counts for
9 papers · 1 filter
Spectral-MoE: Heterogeneous Per-Layer Expert Utilization in Dense-to-MoE Conversion
Zachary Broveak, Christian Kubicka, Thierry Tambe
Large language models impose substantial memory demands and arithmetic costs during inference. Dense-to-mixture-of-experts (MoE) conversion is a model optimization technique that a…
BEHAVE: Functional Behavior Modeling Enables Self-Improving Agents for Hardware Design and Verification
Yuheng Wu, Berk Gokmen, Sujeeth Jinesh +5
Developing agents for hardware design and verification requires reliable correctness feedback. As a hardware specification may permit correct implementations with different latenci…
Academia x Industry: The Role of Fundamentals for Silicon in an AI Native Era
Vincent T. Lee, Armin Alaghi, Carole-Jean Wu +5
Agentic AI is set to become one of the most transformational technologies in generations and materially change how we approach silicon design and engineering. The impact is being f…
Agent Memory: Characterization and System Implications of Stateful Long-Horizon Workloads
Yasmine Omri, Ziyu Gan, Zachary Broveak +6
LLM agents are increasingly deployed on long-horizon tasks requiring sustained reasoning over extended interaction histories. Realizing this at scale requires agents to persistentl…
AgenticCache: Cache-Driven Asynchronous Planning for Embodied AI Agents
Hojoon Kim, Yuheng Wu, Thierry Tambe
Embodied AI agents increasingly rely on large language models (LLMs) for planning, yet per-step LLM calls impose severe latency and cost. In this paper, we show that embodied tasks…
The Hyperscale Lottery: How State-Space Models Have Sacrificed Edge Efficiency
Robin Geens, Jonas De Schouwer, Marian Verhelst +1
The Hardware Lottery posits that research directions are dictated by available silicon compute platforms. We identify a derivative phenomenon, the Hyperscale Lottery, where model a…
SemanticDialect: Semantic-Aware Mixed-Format Quantization for Video Diffusion Transformers
Wonsuk Jang, Thierry Tambe
Diffusion Transformers (DiTs) achieve state-of-the-art video generation quality, but their substantial memory and computational footprints hinder edge deployment. Quantization can…
Heterogeneous Memory Design Exploration for AI Accelerators with a Gain Cell Memory Compiler
Xinxin Wang, Lixian Yan, Shuhan Liu +10
As memory increasingly dominates system cost and energy, heterogeneous on-chip memory systems that combine technologies with complementary characteristics are becoming essential. G…
LLM-FSM: Scaling Large Language Models for Finite-State Reasoning in RTL Code Generation
Yuheng Wu, Berk Gokmen, Zhouhua Xie +4
Finite-state reasoning, the ability to understand and implement state-dependent behavior, is central to hardware design. In this paper, we present LLM-FSM, a benchmark that evaluat…