Hmn-384 'link' May 2026

Report on “HMN‑384”
Prepared: 13 April 2026

7.2 Emerging Materials

Research into phase‑change memristors and ferroelectric tunnel junctions could further reduce write energy and improve weight precision, allowing deeper networks with finer granularity of learning on chip. HMN-384

6. Future Roadmap & Development

| Year | Planned Feature | |------|-----------------| | 2025 | Release of HMN‑384‑V2 with 48‑bit ADC option (up to 10 MS/s) and integrated on‑board AI inference accelerator (Xilinx Versal). | | 2026 | Introduction of a compact 2‑U variant (HMN‑384‑C) for edge deployments, with optional battery operation for field data loggers. | | 2027 | Full cloud‑native integration via a native Azure‑IoT Edge module, enabling real‑time streaming analytics without a local PC. | | 2028 | Open‑source firmware initiative to encourage community‑driven mezzanine development (e.g., custom DSP blocks). | Report on “HMN‑384” Prepared: 13 April 2026

4. Software & Networking

• Use ROS2 Foxy or later (match vendor recommendation).
• Install vendor packages and drivers; source workspace before launching.
• Default user account: hmadmin (change password on first boot).
• Networking:
  • Prefer wired GigE for high-bandwidth sensors (cameras, LiDAR).
  • Use static IP for repeatable SSH and ROS2 node discovery in lab.
• Security: disable unused services, enable SSH keys, restrict Wi‑Fi access to trusted network.

Development and Future Plans

• Development Journey: Share the journey of HMN-384 from conception to its current state. Highlight any significant milestones or challenges overcome.
• Future Roadmap: If available, discuss any planned updates, features, or expansions for HMN-384. What does the future hold for this project?

5.3 Smart Manufacturing

Robotic arms in a factory floor can host an HMN‑384 to perform real‑time force feedback and predictive maintenance. The analog spikes encode tactile events with sub‑microsecond resolution, while the hybrid dense units execute lightweight transformer models that predict component wear, all within a confined thermal envelope suitable for industrial enclosures. discuss any planned updates

2.2 Hyper‑Neural Processing Units (HNPUs)

Each tile can be dynamically re‑configured as one of three Hyper‑Neural Processing Units:

1. Spiking Convolutional Unit (SCU) – Implements event‑driven convolutions for vision and audio front‑ends.
2. Temporal Memory Unit (TMU) – Supports long‑short‑term spiking memory (e.g., Hierarchical Temporal Memory) for sequence learning.
3. Hybrid Dense Unit (HDU) – Couples analog spikes with digital matrix‑multiply engines for transformer‑style attention.

The runtime system (see § 4) partitions a neural model across the mesh, allocating the most suitable HNPU type to each layer. This flexibility is a key differentiator from fixed‑function neuromorphic chips.

2. Results

4. Market Landscape

| Metric (2024) | Value | |---------------|-------| | Total Addressable Market (TAM) | ≈ USD 2.3 B for high‑density DAQ solutions (industrial + scientific). | | HMN‑384 Share | ~ 7 % of TAM (≈ USD 160 M). | | Key Competitors | • National Instruments PXIe‑1085 (max 256 channels)
• Keysight M3102A (128‑channel)
• Teledyne‑LeCroy WaveSurfer 4‑K (high‑speed, low‑channel) | | Competitive Advantages | • Highest channel count in a single chassis
• Modular mezzanine flexibility
• Ruggedized IP‑67 chassis for field deployment | | Growth Drivers | • Expanding autonomous‑vehicle sensor stacks
• Increased telemetry needs for Small‑Sat constellations
• Adoption of AI‑driven real‑time analytics in manufacturing | | Risks | • Supply‑chain constraints for high‑speed ADC dies
• Emerging ASIC‑centric DAQ architectures that integrate processing on the sensor side. |