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Merge pull request #260 from neural-loop/main
#219 feat: Add pulsar by innatera hardware page
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content/neuromorphic-computing/hardware/make.py

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---
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title: "Pulsar - Innatera"
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active_product: true
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description: "Innatera's Pulsar is the first commercially available, brain-inspired microcontroller for sensing at the edge, offering real-time intelligence at microwatt power levels."
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type: neuromorphic-hardware
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image: "pulsar.png"
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organization:
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group_name: null
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org_logo: "innatera.png"
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org_name: "Innatera"
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org_website: "https://www.innatera.com/"
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product_page_link: "https://innatera.com/pulsar"
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social_media_links:
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linkedin: "https://www.linkedin.com/company/innatera/"
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product:
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announced_date: "2025-05-21" # Based on T1, will use this as placeholder
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applications: "Wearables, Hearables, Smart Home, IoT, Industrial Monitoring"
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chip_type: "Mixed-signal"
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neurons: "N/A" # Specific numbers not on page
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synapses: "N/A"
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weight_bits: null
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activation_bits: null
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on_chip_learning: false
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power: "< 1mW (microwatt levels)"
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release_year: 2025
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release_date: "2025-05-21" # Placeholder
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software: "Talamo SDK"
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status:
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announced: true
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released: true
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retired: false
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summary: "Pulsar is a brain-inspired neuromorphic microcontroller designed for ultra-low-power, real-time intelligence in edge devices. It combines a Spiking Neural Network (SNN) engine with a RISC-V MCU and CNN acceleration to enable smart sensing applications without cloud dependency."
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---
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## Overview
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Pulsar is Innatera's revolutionary brain-inspired microcontroller, engineered to deliver real-time, event-driven intelligence for a new generation of smart devices. As the world's first commercially available neuromorphic processor for the mass market, Pulsar is designed to process sensor data with ultra-low power consumption, enabling "always-on" capabilities in wearables, IoT devices, and industrial systems without relying on the cloud.
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The core of Pulsar's innovation lies in its Spiking Neural Network (SNN) engine. Unlike traditional AI that continuously processes dense data, SNNs operate on event-driven spikes, activating compute power only when new information is available. This drastically reduces energy consumption to microwatt levels while achieving sub-millisecond inference speeds.
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## Architecture
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Pulsar features a hybrid architecture that provides both efficiency and flexibility:
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* **Spiking Neural Network (SNN) Engine:** The primary engine for processing sensor data like motion, audio, and vibration with maximum power efficiency.
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* **RISC-V MCU:** A conventional microcontroller unit for general-purpose tasks and control.
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* **CNN Acceleration:** Hardware support for Convolutional Neural Networks, offering versatility across different types of AI workloads.
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This combination allows Pulsar to function as the sole microcontroller a sensor needs, integrating neuromorphic intelligence directly at the edge.
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## Software and Development
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Development for Pulsar is streamlined through the **Talamo SDK**. This software development kit allows developers to:
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- Create Spiking Neural Network models.
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- Easily port existing models from popular frameworks like TensorFlow and PyTorch.
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- Develop applications without requiring deep expertise in neuromorphic engineering.
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This approach lowers the barrier to entry for building brain-inspired AI solutions.
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## Applications
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Pulsar's unique capabilities make it ideal for a wide range of power-constrained and latency-critical applications:
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* **Wearables & Hearables:** Enabling always-on health monitoring, gesture recognition, and fall detection.
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* **Smart Home & IoT:** Providing real-time event detection and environmental awareness without cloud reliance, enhancing privacy and responsiveness.
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* **Industrial Applications:** Facilitating predictive maintenance and anomaly detection through ultra-efficient, continuous monitoring of machinery and systems.
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flake.lock

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flake.nix

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{
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description = "Development environment with Hugo, PostCSS, and Go";
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description = "A dev-shell for the Open Neuromorphic Hugo project";
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inputs = {
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nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
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flake-utils.url = "github:numtide/flake-utils";
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};
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outputs = { self, nixpkgs, flake-utils }:
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let
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system = "x86_64-linux";
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pkgs = import nixpkgs { inherit system; };
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in flake-utils.lib.eachDefaultSystem (system: {
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devShells.default = pkgs.mkShell {
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packages = [
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(pkgs.hugo.overrideAttrs (old: {
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version = "0.147.7";
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}))
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pkgs.nodePackages.postcss
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pkgs.go
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];
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};
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flake-utils.lib.eachDefaultSystem (system:
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let
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pkgs = nixpkgs.legacyPackages.${system};
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in
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{
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devShells.default = pkgs.mkShell {
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buildInputs = with pkgs; [
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hugo
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go_1_24
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nodejs_22
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git
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chromium
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bash-completion
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ncurses # Provides 'tput' for colors
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];
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shellHook = ''
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# Point Puppeteer to the Nix-provided Chromium
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export PUPPETEER_SKIP_CHROMIUM_DOWNLOAD=true
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export PUPPETEER_EXECUTABLE_PATH="${pkgs.chromium}/bin/chromium"
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# Enable history and other interactive features
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set -o vi # or set -o emacs for emacs keybindings
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HISTFILE=~/.bash_history_nix_dev
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history -c
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history -r
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# Set a nice, readable prompt using tput for portability
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if command -v tput >/dev/null && tput setaf 1 >/dev/null; then
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green=$(tput setaf 2)
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blue=$(tput setaf 4)
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reset=$(tput sgr0)
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export PS1="$green[nix-dev]$reset $blue\w$reset$ "
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else
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export PS1='[nix-dev] \w\$ '
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fi
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echo ""
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echo "----------------------------------------------------"
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echo " Welcome to the Open Neuromorphic dev environment! "
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echo ""
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echo " All system dependencies (Hugo, Node.js, Go) are in your PATH."
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echo " 1. Run 'npm install' to get project-specific packages."
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echo " 2. Run 'npm run dev' to start the Hugo server."
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echo "----------------------------------------------------"
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echo ""
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'';
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};
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}
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);
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}

hugo.toml

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########################## Permalinks ############################
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[permalinks.page]
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"pages" = "/:slugorcontentbasename/"
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"pages" = "/:slug/"
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############################# Modules ############################

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