Vendor: OPENEDGES Technology, Inc. Category: NPU

4-/8-bit mixed-precision NPU IP

Features a optimized network model compiler that reduces DRAM traffic from intermediate activation data by grouped layer partitio…

Contact Provider Request Datasheet

Overview

Features a highly optimized network model compiler that reduces DRAM traffic from intermediate activation data by grouped layer partitioning and scheduling. ENLIGHT is easy to customize to different core sizes and performance for customers' targeted market applications and achieves significant efficiencies in size, power, performance, and DRAM bandwidth, based on the industry's first adoption of 4-/8-bit mixed-quantization. 

Performs various operations of deep neural networks such as convolution, pooling, and non-linear activation functions for edge computing environments. This NPU IP far surpasses alternative solutions, delivering unparalleled compute density with energy efficiency (power, performance, and area).

Key features

  • Mixed-Precision (4/8-bit) Computation: Higher efficiency in PPAs and DRAM bandwidth​
  • Deep Neural Network (DNN)-optimized Vector Engine: Better adaptation to future DNN changes
  • Scale-out w/ Multi-core: Even higher performance by parallel processing of DNN layers
  • Modern DNN Algorithm Support: Depth-wise convolution, feature pyramid network (FPN), swish/mish activation, etc.
  • High-level Inter-layer Optimization: Grouped layer partitioning and scheduling for reducing DRAM traffic from intermediate data
  • DNN-layers Parallelization: Efficiently utilize multi-core resources for higher performance & optimize data movements among cores
  • Aggressive Quantization: Maximize use of 4-bit computation capability

Block Diagram

ENLIGHT Classic block diagram

Benefits

  • NN Converter
    • Converts a network file into internal network format (.enlight)​
    • Supports ONNX (PyTorch), TF-Lite, and CFG (Darknet)
  • ​NN Quantizer
    • Generates ​quantized network: float to 4-/8-bit integer
    • Supports per-layer quantization of activation and per-channel quantization of weight 
  • ​NN Simulator
    • Evaluates full precision network and quantized network​
    • Estimates accuracy loss due to quantization
  • ​NN Compiler
    • Generates NPU handling code for target architecture and network​

Applications

  • Person, vehicle, bike, traffic sign detection
  • Parking lot vehicle location detection & recognition
  • License plate detection & recognition
  • Detection, tracking, and action recognition for surveillance

What’s Included?

  • RTL design for synthesis
  • SW toolkits and device driver
  • User guide
  • Integration guide

Specifications

Identity

Part Number
ENLIGHT Classic
Vendor
OPENEDGES Technology, Inc.
Type
Silicon IP

Files

Product Sheet Download

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

Provider

Contact OPENEDGES Technology, Inc.

Learn more about NPU IP core

Benchmarking an NPU at Scale

With every SDK release, Quadric automatically recompiles and re-profiles the entire model zoo across a broad sweep of hardware configurations. The results land in DevStudio for any SoC architect to explore — no sales call, no handpicked numbers.

Your NPU Learned to Listen

Whisper runs end-to-end on Chimera. Encoder and decoder compiled as native GPNPU kernels, INT4 weights, FP16 attention, top-1 token match against the float32 reference. Scales to four cores with a flag, no recompile.

Heterogeneous NPU Data Movement Tax: Intel's Own Slides Tell the Story

At Quadric, we have long argued that heterogeneous NPU designs — those that stitch together multiple specialized fixed-function engines — carry an unavoidable hidden cost: data has to move. A lot. And data movement burns power, adds latency, and creates silicon-area overhead that scales with every new generation of AI models. Now, Intel has made that case for us.

The Upcoming NPU Shakeout

The IP industry is no stranger to boom and bust cycles, and it looks to be at the crest of another wave.

Frequently asked questions about NPU IP cores

What is 4-/8-bit mixed-precision NPU IP?

4-/8-bit mixed-precision NPU IP is a NPU IP core from OPENEDGES Technology, Inc. listed on Semi IP Hub.

How should engineers evaluate this NPU?

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

×
Semiconductor IP