WaveLore Communications is a hardware-focused startup building next-generation systems for
semantic communication. We design specialized hardware and deep learning decoders
that decode semantic-based symbols (audio or video representations) and enable
scalable machine learning compute inside wireless communication pipelines.
Our mission is to make communication more efficient, robust, and meaning-aware by shifting from
“bit-perfect transmission” toward semantic-level understanding.
What we do
Hardware + AI decoding
We build hardware platforms optimized for running deep learning models that decode semantic symbols,
and we develop decoding architectures that convert received semantic symbols into meaning-rich outputs
efficiently at the edge.
Problems we solve
Why it matters
Traditional wireless systems optimize bits even when the true objective is meaning or task output
Semantic decoding with deep learning is compute-heavy and difficult to deploy on scalable, low-power hardware
Wireless ML pipelines require low-latency inference that remains reliable under real channel conditions
Target users
Who it’s for
Primary: wireless and edge-AI teams building next-gen communication systems
Secondary: industrial IoT, robotics, autonomous systems, and critical communications
Early adopters: R&D labs and engineering teams prototyping semantic comms + edge inference hardware
Core Technology
Our stack combines semantic symbol design (audio/video-like representations), deep learning decoding architectures,
and scalable hardware optimized for low-latency inference inside wireless communication systems.
Semantic Symbol Encoding
Semantic symbols represented as audio- or video-like patterns that carry meaning and task intent,
rather than raw bit-level payloads.
Deep Learning Semantic Decoders
Neural architectures that decode received semantic symbols into structured outputs
(meaning, classes, intents, or task-relevant representations).
Scalable ML Hardware for Wireless
Hardware designs optimized for edge inference—throughput, latency, and power efficiency—
to enable real-time ML within wireless communication pipelines.
Prototype + Validation Workflow
Validation using test signals and controlled experiments, with public documentation and diagrams that
show semantic decoding performance and hardware feasibility.
Product
WaveLore Semantic Hardware Platform is a scalable hardware + software stack for semantic communication,
designed to decode semantic-based symbols (audio/video representations) using deep learning architectures and to
support ML inference within wireless systems at the edge.
Core features
What users get
Semantic decoder pipeline — deep learning models that decode semantic symbols into meaning-aware outputs
Hardware-optimized inference — scalable compute design for low-latency semantic decoding at the edge
Symbol experimentation — audio/video semantic symbol formats for robust detection and decoding
Edge semantic communication for robotics and autonomous systems (task-level reliability)
Industrial IoT where low-latency inference is needed over noisy or constrained links
Next-gen wireless R&D for semantic PHY and meaning-aware decoding experiments
Integrations
How it fits
Signal input from SDR/test equipment and replayable datasets
Model deployment on scalable compute (prototype hardware and edge form factors)
Exportable results: plots, logs, performance summaries for reviews and reporting
Diagram 1
Diagram 2
Diagram 3
Development Stage
Current Stage: MVP (Prototype hardware + decoder pipeline in active development)
We are building and validating the MVP through prototype demonstrations, decoder benchmarks, and hardware feasibility tests.
What exists today
Current capabilities
Semantic symbol decoding pipeline (audio/video semantic representations → decoded outputs)
Initial deep learning decoder architectures for semantic inference
Prototype compute setup for measuring latency/throughput/power feasibility
Validation
Evidence of progress
Prototype screenshots/diagrams showing semantic decoding pipeline and outputs
Early benchmarks (accuracy/robustness/latency) and repeatable evaluation scripts
Roadmap for scalable hardware packaging and deployment readiness
Next 30–90 days
Milestones
Publish more public screenshots/diagrams of the product workflow and hardware
Complete next prototype iteration and share performance targets
Expand benchmarks across more symbol formats and wireless conditions
Team
WaveLore Communications is led by founders with complementary expertise across low-energy hardware,
physical-layer wireless systems, and machine learning for semantic communication.
Professional profile links (LinkedIn) are provided for transparency.
William Asiedu
Co-Founder — Low-Energy Hardware in Wireless Communication
Focused on building energy-efficient, scalable hardware architectures for ML-enabled wireless systems,
targeting practical deployment constraints such as latency, throughput, and power.
Leads PHY-layer and ML decoding direction, bridging wireless signal processing and deep learning methods
for semantic symbol decoding under realistic channel conditions.
Works across system design and end-to-end integration: semantic communication concepts, wireless/network architecture,
and ML workflows for deployment-ready decoding pipelines and demonstrations.
