22 citations · 40 across the 25 of their papers we have counts for
28 papers
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…
P3-LLM: An Integrated NPU-PIM Accelerator for Edge LLM Inference Using Hybrid Numerical Formats
Yuzong Chen, Chao Fang, Xilai Dai +4
The substantial memory bandwidth and computational demands of large language models (LLMs) present critical challenges for efficient inference. To tackle this, the literature has e…
On the Role of Temperature Sampling in Test-Time Scaling
Yuheng Wu, Azalia Mirhoseini, Thierry Tambe
Large language models (LLMs) can improve reasoning at inference time through test-time scaling (TTS), where multiple reasoning traces are generated and the best one is selected. Pr…
AttAnchor: Guiding Cross-Modal Token Alignment in VLMs with Attention Anchors
Junyang Zhang, Tianyi Zhu, Thierry Tambe
A fundamental reason for the dominance of attention over RNNs and LSTMs in LLMs is its ability to capture long-range dependencies by modeling direct interactions between all tokens…
The Future of Memory: Limits and Opportunities
Samuel Dayo, Shuhan Liu, Peijing Li +5
Memory latency, bandwidth, capacity, and energy increasingly limit performance. In this paper, we reconsider proposed system architectures that consist of huge (many-terabyte to pe…
GaussianVision: Vision-Language Alignment from Compressed Image Representations using 2D Gaussian Splatting
Yasmine Omri, Connor Ding, Tsachy Weissman +1
Modern vision language pipelines are driven by RGB vision encoders trained on massive image text corpora. While these pipelines have enabled impressive zero-shot capabilities and s…
Towards Memory Specialization: A Case for Long-Term and Short-Term RAM
Peijing Li, Muhammad Shahir Abdurraman, Rachel Cleaveland +6
Both SRAM and DRAM have stopped scaling: there is no technical roadmap to reduce their cost (per byte/GB). As a result, memory now dominates system cost. This paper argues for a pa…
OpenGCRAM: An Open-Source Gain Cell Compiler Enabling Design-Space Exploration for AI Workloads
Xinxin Wang, Lixian Yan, Shuhan Liu +10
Gain Cell memory (GCRAM) offers higher density and lower power than SRAM, making it a promising candidate for on-chip memory in domain-specific accelerators. To support workloads w…
Token Sequence Compression for Efficient Multimodal Computing
Yasmine Omri, Parth Shroff, Thierry Tambe
The exponential growth of Large Multimodal Models (LMMs) has driven advancements in cross-modal reasoning but at significant computational costs. In this work, we focus on visual l…
GainSight: A Unified Framework for Data Lifetime Profiling and Heterogeneous Memory Composition
Peijing Li, Matthew Hung, Yiming Tan +8
As AI workloads drive increasing memory requirements, domain-specific accelerators need higher-density on-chip memory beyond what current SRAM scaling trends can provide. Simultane…
RaZeR: Pushing the Limits of NVFP4 Quantization with Redundant Zero Remapping
Yuzong Chen, Xilai Dai, Jake Hyun +6
The recently introduced NVFP4 format demonstrates remarkable performance and memory benefits for quantized large language model (LLM) inference. However, we observe two types of re…
BlockDialect: Block-wise Fine-grained Mixed Format Quantization for Energy-Efficient LLM Inference
Wonsuk Jang, Thierry Tambe
The rapidly increasing size of large language models (LLMs) presents significant challenges in memory usage and computational costs. Quantizing both weights and activations can add…
VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning
Yu-Shun Hsiao, Siva Kumar Sastry Hari, Balakumar Sundaralingam +5
High-dimensional motion generation requires numerical precision for smooth, collision-free solutions. Typically, double-precision or single-precision floating-point (FP) formats ar…
CAMEL: Co-Designing AI Models and Embedded DRAMs for Efficient On-Device Learning
Sai Qian Zhang, Thierry Tambe, Nestor Cuevas +2
On-device learning allows AI models to adapt to user data, thereby enhancing service quality on edge platforms. However, training AI on resource-limited devices poses significant c…
Application-Level Validation of Accelerator Designs Using a Formal Software/Hardware Interface
Bo-Yuan Huang, Steven Lyubomirsky, Yi Li +10
Ideally, accelerator development should be as easy as software development. Several recent design languages/tools are working toward this goal, but actually testing early designs o…
AutoSoC: Automating Algorithm-SOC Co-design for Aerial Robots
Srivatsan Krishnan, Thierry Tambe, Zishen Wan +1
Aerial autonomous machines (Drones) has a plethora of promising applications and use cases. While the popularity of these autonomous machines continues to grow, there are many chal…