RISC-V指令集架构
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爱普特微电子董事长李炜:优化芯片产业政策,打破芯片产业传统路径依赖
Sou Hu Cai Jing· 2026-02-11 11:53
Core Viewpoint - The RISC-V instruction set architecture, characterized by its open-source nature, presents significant opportunities to break the traditional path dependence in the chip industry and establish a self-controlled computing foundation [1] Group 1: Industry Insights - Shenzhen has developed a leading industrial cluster in high-performance RISC-V microcontrollers (MCUs) [3] - Current integrated circuit industry support policies primarily focus on advanced logic processes of 28nm and below, which does not align with the RISC-V chips that mainly utilize mature processes of 55nm and 40nm in strategic fields such as automotive electronics and IoT [3] - The mismatch in policy direction restricts local enterprises from breaking into high-value markets, hindering Shenzhen's strategic advantage in the open-source chip ecosystem [3] Group 2: Policy Recommendations - It is suggested to establish a special fund within the municipal industrial funds to subsidize RISC-V chip projects that adopt mature processes like 55nm and 40nm for initial engineering tape-outs [4] - Reforming the subsidy policies for IP and EDA tools is recommended to support the core toolchain of the RISC-V ecosystem, with a focus on projects using domestic or open-source RISC-V IP [4] - In consumer electronics sectors where Shenzhen has advantages, it is proposed to facilitate connections between chip design companies and terminal manufacturers to promote application demonstration projects that prioritize local RISC-V chip solutions [4]
国芯科技(688262.SH):研发的神经网络处理器DPNPU新IP产品内部测试成功
Ge Long Hui A P P· 2026-01-04 10:51
国芯科技 DPNPU 单核支持 0.5~4.8 TOPS 的灵活算力配置,支持算力线性扩展,可以为不同场景提供 定制化的 AI 算力解决方案。国芯科技DPNPU采用符合 RISC-V 指令集架构(RISC-V ISA)标准的创新 开放架构,该架构通过将RISC-V 核心与高性能神经网络加速单元在架构层面进行深度优化设计,并设 计了专用的 TDS(Task Distribution&Synchronization)硬件调度引擎作为核心控制单元,将网络模型中 的算子序列转化为高效的节点化任务流,实现了任务管理、数据流控制与 AI 专用计算的统一高效调 度。该新 IP 产品采用脉动阵列高效动态融合技术,确保设备在长时间运行中的稳定性和卓越能效表 现。DPNPU内置90+神经网络算子,全面覆盖 CNN、RNN 神经网络架构,并支持LSTM、GRU等RNN 变体。通过 RISC-V 指令和硬件通用性设计可扩展支持更多算子,为适配未来不断涌现的 AI 模型预留 充足空间。DPNPU 支持训练后量化(PTQ)技术,提供对称量化、非对称量化、逐层量化和逐通道量 化四种方式。同时支持INT8和 FP16 混合精度量化,在保持模 ...