AI Archives

Oct 16

Optimizing Hardware for Neural Network Inference using Virtual Prototypes

By RISC-V International Staff Featured Work

Project Snapshot Identifying the optimal hardware configuration for running NN inference on edge devices is critical for maximizing performance. Tailoring HW designs to specific applications significantly increases resource utilization. We…

Sep 26

ElectroPages: RISC-V Acceleration for Deep Learning at the Edge

By Anisha Sharma In the Media

By running an open-source NVIDIA Deep Learning Accelerator directly on a RISC-V chip (without a traditional operating system), a group of researchers has achieved impressive performance and energy efficiency.

Sep 19

22nm RISC-V AI Chip Targets Wearables and IoT

By Anisha Sharma In the Media

EMASS has introduced the ECS-DoT, a 22nm microprocessor designed to bring milliWatt-scale intelligence directly to edge and IoT devices.

Sep 08

SiFive Expands Its RISC-V Intelligence Family To Address Exploding AI Workloads

By Anisha Sharma In the Media

SiFive just announced an array of new additions to its product stack that run the gamut, from tiny, ultra lower-power designs for far edge IoT devices, to more powerful engines…

Sep 05

RISC-V: The AI-Native Platform for the Next Trillion Dollars of Compute

By Makeljana Shkurti Blog

We explore how companies across industries are proving the viability of RISC-V as a native architecture for modern AI workloads

Aug 27

Arteris’ Multi-Die Solution for the RISC-V Ecosystem

By Anisha Sharma Blog

by Ashley Stevens, Director of Product Management and Marketing at Arteris The amount of compute used to train frontier AI models has been doubling roughly every five to six months, but…

Aug 07

NVIDIA on RVA23: “We Wouldn’t Have Considered Porting CUDA to RISC-V Without It”

By James De Vile Viewpoints

By setting a clear, stable standard, the RVA23 profile’s ratification is spurring top vendors to align on a common RISC-V hardware goal. All we need now is that hardware to…

Aug 05

Enabling Space-Grade AI/ML with RISC-V: A Fully European Stack for Autonomous Missions

By Anisha Sharma In the Media

As modern space missions evolve in complexity, the role of software onboard spacecraft is undergoing a dramatic transformation. Spacecraft are no longer limited to basic telemetry and remote control. Today, onboard computing…

Jul 20

Nvidia’s CUDA platform now supports RISC-V — support brings open source instruction set to AI platforms, joining x86 and Arm

By Anisha Sharma In the Media

At the 2025 RISC-V Summit in China, Nvidia announced that its CUDA software platform will be made compatible with the RISC-V instruction set architecture (ISA) on the CPU side of…

Jul 09

GlobalFoundries to Acquire MIPS to Accelerate AI and Compute Capabilities

By Anisha Sharma In the Media

MALTA, N.Y. and SAN JOSE, Calif., July 08, 2025 (GLOBE NEWSWIRE) -- GlobalFoundries (Nasdaq: GFS) (GF) today announced a definitive agreement to acquire MIPS, a leading supplier of AI and…

AI

Optimizing Hardware for Neural Network Inference using Virtual Prototypes

ElectroPages: RISC-V Acceleration for Deep Learning at the Edge

22nm RISC-V AI Chip Targets Wearables and IoT

SiFive Expands Its RISC-V Intelligence Family To Address Exploding AI Workloads

RISC-V: The AI-Native Platform for the Next Trillion Dollars of Compute

Arteris’ Multi-Die Solution for the RISC-V Ecosystem

NVIDIA on RVA23: “We Wouldn’t Have Considered Porting CUDA to RISC-V Without It”

Enabling Space-Grade AI/ML with RISC-V: A Fully European Stack for Autonomous Missions

Nvidia’s CUDA platform now supports RISC-V — support brings open source instruction set to AI platforms, joining x86 and Arm

GlobalFoundries to Acquire MIPS to Accelerate AI and Compute Capabilities

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About

Policies

Specification

Developers

Industries

Events

Members

Resources

Join RISC-V International

Becoming a member of RISC-V International allows companies and individuals to actively influence the development of an open, royalty-free instruction set architecture, driving innovation in custom processor designs.