Lattice sensAI
E656342
Lattice sensAI is Lattice Semiconductor’s low-power, FPGA-based edge AI platform designed to accelerate machine learning inference in resource-constrained applications.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Lattice sensAI canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T7328794 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Lattice sensAI Context triple: [Lattice Semiconductor, offersPlatform, Lattice sensAI]
-
A.
Adobe Sensei AI platform
Adobe Sensei AI platform is Adobe’s artificial intelligence and machine learning framework that powers intelligent features across its creative and experience products, enabling automated, context-aware tools for tasks like image editing, personalization, and content optimization.
-
B.
Adept AI
Adept AI is an artificial intelligence research and product company focused on building AI agents that can use existing software tools to perform complex tasks for users.
-
C.
Goya inference processor
The Goya inference processor is Habana Labs’ specialized AI chip designed to accelerate deep learning inference workloads with high performance and efficiency.
-
D.
Kurzweil Applied Intelligence
Kurzweil Applied Intelligence is a technology company known for pioneering speech recognition and artificial intelligence software applications.
-
E.
Tensor Processing Unit
A Tensor Processing Unit (TPU) is a specialized AI accelerator chip designed by Google to efficiently perform large-scale machine learning computations, particularly for neural networks.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Lattice sensAI Target entity description: Lattice sensAI is Lattice Semiconductor’s low-power, FPGA-based edge AI platform designed to accelerate machine learning inference in resource-constrained applications.
-
A.
Adobe Sensei AI platform
Adobe Sensei AI platform is Adobe’s artificial intelligence and machine learning framework that powers intelligent features across its creative and experience products, enabling automated, context-aware tools for tasks like image editing, personalization, and content optimization.
-
B.
Adept AI
Adept AI is an artificial intelligence research and product company focused on building AI agents that can use existing software tools to perform complex tasks for users.
-
C.
Goya inference processor
The Goya inference processor is Habana Labs’ specialized AI chip designed to accelerate deep learning inference workloads with high performance and efficiency.
-
D.
Kurzweil Applied Intelligence
Kurzweil Applied Intelligence is a technology company known for pioneering speech recognition and artificial intelligence software applications.
-
E.
Tensor Processing Unit
A Tensor Processing Unit (TPU) is a specialized AI accelerator chip designed by Google to efficiently perform large-scale machine learning computations, particularly for neural networks.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
FPGA-based AI solution
ⓘ
edge AI platform ⓘ |
| basedOn |
Lattice FPGA devices
ⓘ
low-power FPGAs ⓘ |
| compatibleWith |
Lattice Certus-NX FPGAs
NERFINISHED
ⓘ
Lattice CrossLink-NX FPGAs NERFINISHED ⓘ Lattice ECP5 FPGAs NERFINISHED ⓘ Lattice iCE40 UltraPlus FPGAs NERFINISHED ⓘ |
| designedFor |
edge AI applications
ⓘ
resource-constrained applications ⓘ |
| developer | Lattice Semiconductor NERFINISHED ⓘ |
| focusesOn |
battery-powered devices
ⓘ
cost-sensitive applications ⓘ small form factor designs ⓘ |
| goal | accelerate deployment of AI at the edge ⓘ |
| includes |
AI/ML IP cores
ⓘ
FPGA hardware ⓘ reference designs ⓘ software tools ⓘ |
| industry |
artificial intelligence
ⓘ
embedded systems ⓘ semiconductors ⓘ |
| marketedAs | low-power edge AI platform ⓘ |
| optimizationTarget |
low latency inference
ⓘ
low power consumption ⓘ |
| ownedBy | Lattice Semiconductor Corporation NERFINISHED ⓘ |
| provides |
hardware-software solution stack
ⓘ
model optimization tools ⓘ performance and power estimation tools ⓘ pre-verified neural network accelerators ⓘ |
| region | global market ⓘ |
| supports |
Caffe model import (via tools)
ⓘ
TensorFlow model import (via tools) ⓘ always-on AI features ⓘ convolutional neural networks ⓘ deep learning inference ⓘ edge inferencing workloads ⓘ int8 inference ⓘ machine learning inference ⓘ quantized neural networks ⓘ real-time analytics at the edge ⓘ |
| technologyType |
hardware acceleration for AI
ⓘ
reconfigurable computing ⓘ |
| useCase |
IoT edge devices
ⓘ
computer vision at the edge ⓘ embedded vision systems ⓘ industrial automation ⓘ smart cameras ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Lattice sensAI Description of subject: Lattice sensAI is Lattice Semiconductor’s low-power, FPGA-based edge AI platform designed to accelerate machine learning inference in resource-constrained applications.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.