5 papers · 1 filter
Fengshui: Demystifying Chiplet Ecosystem and Bespoke Neural Network Accelerator Codesign
Haoran Jin, Jirong Yang, Zhiheng Zhang +5
Modern ML workloads, with stringent latency and energy constraints, are increasingly hard to run efficiently on homogeneous commodity hardware. We argue that operator-level disaggr…
Rethinking Agentic Kernel Generation for Emerging Accelerators
Ruijie Gao, Jirong Yang, Barry Lyu +2
Emerging accelerators often lack mature compiler backends, motivating neural agents that generate and repair kernels from architectural documentation and simulator feedback. This a…
TensorLift: Automatic Extraction of Tensor-Level ISA Semantics from Accelerator RTL via MLIR Semantic Lifting
Ruijie Gao, Haoran Jin, Jirong Yang +1
Most proposed tensor accelerators lack well-documented ISAs and compiler backends, and are exercised only through hand-written kernels covering a handful of operators. Recent work…
Mozart: A Chiplet Ecosystem-Accelerator Codesign Framework for Composable Bespoke Application Specific Integrated Circuits
Haoran Jin, Jirong Yang, Yunpeng Liu +3
Modern AI acceleration faces a fundamental challenge: conventional assumptions about memory requirements, batching effectiveness, and latency-throughput tradeoffs are systemwide ge…
Understanding Interactions Between Chip Architecture and Uncertainties in Semiconductor Supply and Demand
Ramakrishna Kanungo, Swamynathan Siva, Nathaniel Bleier +3
Mitigating losses from supply and demand volatility in the semiconductor supply chain and market has traditionally been cast as a logistics and forecasting problem. We investigate…