Vendor: Blumind Category: Edge AI Accelerator

All-analog Neural Signal Processor

Empowering Innovations with Blumind's Proprietary Architecture Unlock the true potential of analog AI compute with Blumind's cutt…

TSMC 22nm ULL View all specifications

Overview

Empowering Innovations with Blumind's Proprietary Architecture

Unlock the true potential of analog AI compute with Blumind's cutting-edge semiconductor architecture, combining machine learning, precision analog signal processing, and brain-inspired computing.

Blumind delivers high performance all-analog compute solutions for edge AI using CMOS technology on advanced process nodes. No special processing steps, no specialty memory structures, no technology risks.

Key features

  • Analog AI Innovation: Blumind AMPL™ is a disruptive analog AI compute fabric for micropower artificial intelligence applications.
  • Precision and Accuracy: Blumind all-analog AI compute delivers deterministic and precise inferencing performance at up to x1000 lower power than our competitors. Delivering higher efficiency and the longest battery life for always-on applications.
  • Low Latency Solutions: AMPL™ fabric delivers efficient low latency for real-time applications.
  • Analog Breakthrough: AMPL™ is the first all-analog AI on advanced standard CMOS architected to fundamentally mitigate process, voltage, temperature and drift variations.

Benefits

  • Ultra low power
  • Compute-in-transistor tiny form factor
  • standard CMOS ptocess (22nm/28nm)

Applications

  • Smart watches
  • True wireless stereo headphones
  • Fitness bands
  • wearables
  • security
  • smoke detectors
  • remote controls
  • smart sensors

Files

Note: some files may require an NDA depending on provider policy.

Silicon Options

Foundry Node Process Maturity
TSMC 22nm ULL

Specifications

Identity

Part Number
AMPL
Vendor
Blumind

Provider

Blumind
HQ: Canada
Blumind was established in 2020 in Ontario, Canada. Our vision is to mimic the human brain as closely as possible by creating an all-analog AI neural network architecture with the most efficient power profile possible. The key was to solve PVT and drift challenges, inherent in other analog AI solutions, while maximizing performance and reliability. We are achieving this using standard, cost-effective CMOS process technology. Blumind delivers easy to use all analog solutions that are 100-1000X more efficient than legacy digital approaches enabling AI for everyone, everywhere.

Learn more about Edge AI Accelerator IP core

RISC-V Based TinyML Accelerator for Depthwise Separable Convolutions in Edge AI

While lightweight architectures like MobileNetV2 employ Depthwise Separable Convolutions (DSC) to reduce computational complexity, their multi-stage design introduces a critical performance bottleneck inherent to layer-by-layer execution: the high energy and latency cost of transferring intermediate feature maps to either large on-chip buffers or off-chip DRAM. To address this memory wall, this paper introduces a novel hardware accelerator architecture that utilizes a fused pixel-wise dataflow.

Accelerating Your Development: Simplify SoC I/O with a Single Multi-Protocol SerDes IP

Enter the Multi-Protocol SerDes (Serializer/Deserializer)—a flexible, reusable IP block that allows a single PHY to support multiple serial communication protocols, such as PCIe, SATA, Ethernet, USB, and more. This approach enables SoC vendors to meet diverse customer requirements and application needs without redesigning I/O for each target market.

Frequently asked questions about Edge AI Accelerator IP cores

What is All-analog Neural Signal Processor?

All-analog Neural Signal Processor is a Edge AI Accelerator IP core from Blumind listed on Semi IP Hub. It is listed with support for tsmc.

How should engineers evaluate this Edge AI Accelerator?

Engineers should review the overview, key features, supported foundries and nodes, maturity, deliverables, and provider information before shortlisting this Edge AI Accelerator IP.

Can this semiconductor IP be compared with similar products?

Yes. Buyers can compare this product with similar semiconductor IP cores or IP families based on category, provider, process options, and structured technical specifications.

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