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20 results for “Basic knowledge of SIMD and SIMT architectures”

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cs.AREmpiricalRecentJul 24, 2026

The Sparsity Tax: Weight Sparsity Trade-offs in Event-Driven SIMD and SIMT Neuromorphic Cores

Mattias Westerink, Sameed Sohail, Berend-Jan van der Zwaag, Sabih Gerez +1 more

This paper compares three neuromorphic core designs for handling weight sparsity in event-driven neural networks and quantifies the performance and energy costs.

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cs.ARcs.LGcs.OSEmpiricalRecentJul 16, 2026

ExaGEMM: Exploration Framework for CPU-Driven ML Inference via Associative In-Register Computing for Low-Bit GEMM

Hyunwoo Oh, Suyeon Jang, Hanning Chen, Sanggeon Yun +2 more

The paper presents ExaGEMM, a framework for designing and exploring CPU-native low-bit GEMM via register-resident LUT execution.

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cs.PLcs.AREmpiricalRecentJul 21, 2026

High-Level Synthesis of Efficient Pipelines with Visibility Control

Jungin Rhee, Minseong Jang, Jaewoo Kim, Jeehoon Kang

A new HLS tool is presented that enables fine-grained pipeline control in a sequential programming model for competitive PPA.

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cs.PLTheoreticalRecentJul 21, 2026

Formal Verification of an Out-of-Order Multiprocessor against an In-Order Weak-Memory ISA

Janggun Lee, Jeehoon Kang

The first formal verification of an out-of-order multiprocessor against an in-order, weak-memory ISA using a core specification and mechanized proofs in Rocq.

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cs.AReess.SYEmpiricalRecentJul 24, 2026

Reducing Instruction-Fetch Energy in RISC-V for Embedded AI Processing via Dynamic and Static Loop Caching

Wiebren Wijnstra, Sameed Sohail, Berend-Jan van der Zwaag, Sabih Gerez +1 more

This paper presents two loop cache architectures for RISC-V processors to reduce instruction fetches and energy consumption during AI inference.

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cs.ARcs.LGEmpiricalRecentJun 11, 2026

BigPower: Hierarchical Source-Level Module Power Estimation for CPUs with Large Language Models

Honghua Zhu, Chunjie Luo, Jianfeng Zhan

This paper introduces BigPower, a hierarchical source-level surrogate model for fine-grained module-level power estimation during CPU design using large language models and architectural hierarchy.

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cs.CRcs.ARcs.LGRecentMar 20, 2026

Hawkeye: Reproducing GPU-Level Non-Determinism

Erez Badash, Dan Boneh, Ilan Komargodski, Megha Srivastava

Hawkeye is a system that allows perfect, precision-preserving reproduction of GPU-level matrix multiplication operations on a CPU, enabling efficient and trustworthy third-party auditing of machine le…

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cs.DScs.DCcs.MSEmpiricalRecentJul 27, 2026

Right Multiplication on Grammar-Compressed Matrices: A Streaming, Memory-Bounded GPU Engine

Francesco Tosoni, Gabriele Mencagli

This paper presents a method for compressing matrices using a RePair straight-line program (SLP), allowing matrix-vector products with time and space proportional to the compressed size, and demonstra…

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cs.AREmpiricalRecentJul 17, 2026

From Patterns to Parsers: Automatic Generation of Efficient Hardware Parsers for FPGAs

Tushar Garg, Andrew Boutros

This paper presents an open-source tool for generating efficient hardware parsers from high-level specifications using a decoupled parsing intermediate representation and custom symbolic tokens.

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cs.CRcs.OSRecentMay 30, 2026

Beyond Edge Coverage: Per-Task Data-Flow Extraction at Kernel Function Boundaries via LLVM

Yunseong Kim

The paper introduces BOUNDARY FLOW, an LLVM-based framework that enhances kernel fuzzing and analysis by extracting per-task, state-aware data-flow information (arguments and return values) at functio…

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cs.ARcs.AIcs.SERecentJun 2, 2026

HighTide: An Agent-Curated Open-Source VLSI Benchmark Suite

Benjamin Goldblatt, Paolo Pedroso, Farhad Modaresi, Ethan Sifferman +1 more

HighTide is an evolving, AI-assisted, open-source benchmark suite for VLSI design, providing a comprehensive and scalable platform for hardware development.

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cs.OScs.CREmpiricalRecentJun 23, 2026

Kops: Safely Extending the eBPF Compilation Pipeline with Native Operations

Yusheng Zheng, Zhengjie Ji, Weichen Tao, Hao Sun +3 more

The paper introduces Kops, an extension interface for eBPF that allows userspace compilers and kernel modules to introduce new operations without modifying the kernel core, improving performance and r…

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cs.CRcs.DCRecentMar 24, 2026

n-VM: A Multi-VM Layer-1 Architecture with Shared Identity and Token State

Jian Sheng Wang

The paper proposes n-VM, a novel Layer-1 architecture that unifies multiple heterogeneous virtual machines (VMs) onto a shared consensus and state layer, solving cross-chain fragmentation issues.

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cs.ARcs.AIRecentMay 30, 2026

LP5X-PIM Sim: A High-Fidelity HW/SW Integrated Simulator for LPDDR5X-PIM

SangHoon Cha, Jaewan Choi, Byeongho Kim, Yoonah Paik +2 more

This paper introduces a high-fidelity, integrated hardware-software simulator for LPDDR5X-PIM, enabling precise evaluation of system performance and energy efficiency.

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cs.MScs.DCmath.NAEmpiricalRecentJul 8, 2026

Multiple Double Arithmetic on NVIDIA Tensor Cores

Howard Chen, Jan Verschelde

The paper presents a solution to enable multiple double arithmetic on NVIDIA A100 tensor cores, which are unsuited for branching operations caused by renormalization.

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cs.CRcs.ARcs.LORecentApr 25, 2026

From Language to Logic: Bridging LLMs & Formal Representations for RTL Assertion Generation

Nowfel Mashnoor, Hadi Kamali, Kimia Azar

The paper introduces ProofLoop, a novel ReAct agent that uses a solver-in-the-loop approach to automatically generate and formally verify SystemVerilog Assertions (SVA) from natural language specifica…

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cs.PLTheoreticalRecentJul 1, 2026

Trustworthy Runtime Verification via Bisimulation (Extended Experience Report)

Ryan G. Scott, Ivan Perez, Alwyn E. Goodloe, Mike Dodds +1 more

This paper presents CopilotVerifier, a tool that generates proofs of correctness for the compiled output of the Copilot runtime verification framework.

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cs.DCEmpiricalRecentJul 9, 2026

On the Limitations of Non-GPU AI Accelerators for Large-Model Inference: A Field Study of MoE and Multimodal Serving on Huawei Ascend

Zheng Yu

The paper presents a field study on deploying large inference workloads on a non-GPU AI accelerator and identifies eight categories of limitations.

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