https://www.perplexity.ai/search/visit-portals-like-reddit-and-Vz.9cnrXQ92.713RFwyDdw?sm=d
<img src="https://r2cdn.perplexity.ai/pplx-full-logo-primary-dark%402x.png"; style="height:64px;margin-right:32px"/> # Visit portals like reddit and tell me about what people are excited about and cooperating around, regarding community edge AI downscaling solutions like networked embedded riscv chips, or other cool approaches. What can people build, plug in, and research on to? People are very actively hacking on “edge AI” as something you can run in a homelab, on cheap boards, and in small community projects, with a lot of energy around RISC‑V, Pi‑class SBCs, NPUs/TPUs, and TinyML.[^1_1][^1_2][^1_3][^1_4][^1_5] Below is a quick map of what people are excited about and concrete things you could build, plug in, or research. ## Themes people are excited about - **RISC‑V edge AI accelerators.** Folks in /r/RISCV and /r/FPGA are into designing custom RISC‑V cores plus AI accelerators on FPGA, or SoCs like the AON1120 (RISC‑V core + NPU + DSP for always‑on audio/vision) as ultra‑low‑power edge nodes.[^1_6][^1_7][^1_3] - **Pi‑class boards + external NPUs.** There’s a big wave of “real‑time AI on Raspberry Pi 5 with Google Coral / Hailo” object detection, etc., often wrapped in Docker and simple scripts so others can reproduce them.[^1_8][^1_4] - **Tiny fully‑local agents.** People in /r/raspberry_pi and /r/LocalLLaMA are showing off “tiny fully local AI agents” on Pi‑class boards, using heavy quantization and lightweight runtimes (e.g., MNN, TFLite, custom C++ inference) to squeeze LLMs into small RAM/CPU envelopes.[^1_9][^1_4][^1_10] - **TinyML for sensor networks.** Embedded+ML folks talk a lot about TinyML/TensorFlow Lite Micro for smart sensors (acoustic deforestation monitors, anomaly detection on IoT nodes, etc.), i.e., running inference on microcontrollers instead of cloud.[^1_5] - **Collaborative ML projects.** In more general ML subs, there are recurring “let’s form a small team and build one real‑world ML project, put it on GitHub with docs and a demo” posts—software‑oriented, but the same pattern could be applied to hardware/edge ML.[^1_11][^1_12] ## Hardware you can build or plug in ### Popular “edge AI brick” options | Thing | What people do with it | Why it’s exciting | | :-- | :-- | :-- | | Raspberry Pi 5 + Google Coral / Hailo | Real‑time object detection from a USB camera, running in Arm64 Docker images with auto‑pulled models and web UIs.[^1_8][^1_4] | Plug‑and‑play, lots of tutorials; easy for others to replicate at home. | | RISC‑V SBCs (e.g. Banana Pi‑V, Milk‑V) | Running Linux + Ollama‑style LLM inference, or experimenting with RISC‑V AI extensions and NPUs.[^1_2][^1_7] | Open ISA, chance to play with vector extensions and custom accelerators. | | Ultra‑low‑power RISC‑V SoCs (e.g. AON1120) | Always‑on voice/audio/gesture detection at a few hundred µW.[^1_3] | Batteries or energy‑harvesting; dense sensor meshes. | | Microcontrollers + TinyML (e.g. Pico‑class boards) | Keyword spotting, vibration anomaly detection, tiny classifiers.[^1_7][^1_5] | Cost is a few dollars per node; you can scale out huge swarms. | ## Concrete project patterns you could pursue ### 1. Networked RISC‑V or MCU sensor swarm - Build a mesh of **RISC‑V or MCU nodes** each running TinyML (audio, vibration, environmental sensors). Use ultra‑low‑power SoCs like AON1120‑class devices for always‑on tasks.[^1_3][^1_5] - Each node does local feature extraction + classification, forwarding only events/embeddings to a central hub. - Community angle: publish open hardware + firmware, plus a data‑sharing backend so others can deploy nodes in different cities and pool anonymized events. ### 2. Pi‑plus‑TPU/Hailo edge inference box - Package a **Pi 5 + Coral/Hailo** stack into a reusable box: plug in USB camera, visit a web page, get object detection or simple agents.[^1_4][^1_8] - Add a simple gRPC/MQTT interface so other boxes (MCUs, RISC‑V sensors) can offload heavier inference to it. - Community angle: define a simple, versioned inference API and a container image; others can run compatible nodes and swap models. ### 3. Custom RISC‑V AI accelerator on FPGA - Follow the patterns discussed in RISC‑V/FPGA threads: implement a pipelined RISC‑V core + small systolic array or vector MAC unit, targeted at small CNNs or tiny transformers.[^1_7][^1_6] - Expose it via AXI‑4 or similar so a soft‑core or host can drive it; publish bitstreams and a C API. - Community angle: invite others to contribute new ops (attention kernels, quantization schemes), and compare designs across boards. ### 4. Tiny fully‑local agent framework - Take the “tiny fully local agent on Pi” idea and turn it into a generic **edge agent framework**: plug‑in tools for GPIO, MQTT, cameras, TTS, etc., plus a set of curated 4‑bit/gguf models.[^1_10][^1_9] - Optimize runtimes with engines like MNN/TFLite, with per‑device model configs (Pi, RISC‑V SBC, x86 NUC).[^1_9][^1_4] - Community angle: shared “skills” repos, hardware recipes (“agent on a stick,” “agent in a wall panel”), and performance leaderboards for various boards. ### 5. Open TinyML benchmark network - Inspired by TinyML and deforestation/IoT examples, you could define a **standard dataset + benchmark** for specific edge tasks (sound event detection, machinery anomalies, etc.).[^1_5] - Participants run them on their own boards (RISC‑V, ARM, FPGA accelerators) and contribute results plus power measurements. - Community angle: a public site/repo where anyone can submit scores and hardware notes, like MLPerf but for ultra‑small edge hardware. ## How to “plug in” and collaborate - Join hardware‑oriented subs like /r/RISCV, /r/embedded, /r/FPGA, /r/raspberry_pi, and edge‑ML subs like /r/LocalLLaMA, /r/ArtificialInteligence, and /r/learnmachinelearning.[^1_2][^1_12][^1_1][^1_11][^1_5] - Look for “forming a small team” or “graduation project combining embedded and ML” style posts and propose hardware/edge‑first angles.[^1_12][^1_11][^1_5] - Publish end‑to‑end recipes: exact BOM, flashing steps, Dockerfiles, and demo videos, since reproducibility is what gets the most traction in these communities.[^1_8][^1_4][^1_10] If you tell me your preferred envelope (e.g., “\$10 RISC‑V MCUs in a mesh” vs “\$100 Pi‑class SBC with NPU”), I can sketch a concrete architecture and a first‑pass protocol for a community edge‑AI network. <span style="display:none">[^1_13][^1_14][^1_15]</span> <div align="center">⁂</div> [^1_1]: https://www.reddit.com/r/embedded/comments/1q29tqw/whats_the_current_state_of_edge_ai_any_recent/ [^1_2]: https://www.reddit.com/r/RISCV/comments/1h7fsms/ai_models_on_risc_v/ [^1_3]: https://www.reddit.com/r/RISCV/comments/19f4taq/aon1120_is_a_ultralowpower_riscv_soc_designed_for/ [^1_4]: https://www.reddit.com/r/raspberry_pi/comments/1f0w2v2/how_to_setup_realtime_ai_on_pi_5_with_google_tpu/ [^1_5]: https://www.reddit.com/r/embedded/comments/nxt7qn/ideas_suggestion_for_graduation_project_combining/ [^1_6]: https://www.reddit.com/r/FPGA/comments/1ox4fit/which_of_these_projects_will_stand_out_the_most/ [^1_7]: https://www.reddit.com/r/RISCV/comments/1hgk22p/riscv_based_ai_accelerator/ [^1_8]: https://www.reddit.com/r/BlueIris/comments/12q3e2p/codeprojectai_211_is_available_with_coral_tpu/ [^1_9]: https://www.reddit.com/r/LocalLLaMA/comments/1npo93e/i_built_a_tiny_fully_local_ai_agent_for_a/ [^1_10]: https://www.reddit.com/r/raspberry_pi/comments/1nq1le3/i_built_a_tiny_fully_local_ai_agent_for_a/ [^1_11]: https://www.reddit.com/r/ArtificialInteligence/comments/1qlq4vp/looking_to_collaborate_on_a_realworld_ml_project/ [^1_12]: https://www.reddit.com/r/learnmachinelearning/comments/1qlq2qd/looking_to_collaborate_on_a_realworld_ml_project/ [^1_13]: https://www.reddit.com/r/RISCV/comments/1oofceb/riscv_explained_riscv_extensions_for_ai/ [^1_14]: https://www.reddit.com/r/singularity/comments/14p9cw8/an_ai_model_designed_a_functional_riscv_cpu_in/ [^1_15]: https://www.reddit.com/r/FPGA/comments/14oogmy/chinese_researchers_used_ai_to_design_riscv_cpu/ --- # what's the cheapest one could build something that independently pursues some kind of general task offline? For “independently pursuing some kind of general task offline,” the current price floor is roughly split between ultra‑cheap TinyML nodes (well under 5 USD in small quantity, sub‑1 USD in volume) and ~5–10 USD class Wi‑Fi/BT MCUs that can host a small agent loop. ## Two practical cost floors ### 1. “Single‑purpose tiny agent” (sub‑\$5, down to ≈\$1) If you’re okay with a narrow but autonomous behavior (sense → run a learned model → act/decide locally), you can get extremely cheap: - TinyML on generic MCUs: - 32‑bit MCUs in the tens‑of‑cents range can run small TinyML models with TensorFlow Lite Micro or similar, using only a few kB of RAM and <1 mW.[^2_1][^2_2][^2_3] - Articles on TinyML note that MCUs suitable for this can be as cheap as about 0.50 USD in volume.[^2_1] - 1‑dollar TinyML boards: - The “1 Dollar TinyML” project hits ≈1 USD BOM for a board with STM32‑like MCU, BLE module, lithium power circuitry, and sensors (accelerometer, microphones, light), explicitly targeting swarms of ML‑enabled nodes.[^2_4][^2_5] - Behavior: - These nodes can learn or receive a small model (e.g., motion anomaly detection, keyword spotting, gesture classification) and then run entirely offline, reacting to the environment per a local policy.[^2_6][^2_2][^2_7] With this style, “independently pursuing a task” means “keep observing, scoring, and taking local actions according to a tiny model/policy,” and you can get into the \$1–3 range per node if you’re willing to DIY the PCB and assembly.[^2_5][^2_4] ### 2. “Mini general agent” (≈\$5–10 and up) If you want something more like a tiny agent loop (state + tools + planning + simple model) rather than a single classifier, the cheapest off‑the‑shelf baseline today is typically ESP32‑class or Nano‑class boards: - ESP32 / ESP32‑S3 boards: - Reddit TinyML folks recommend ESP32 variants as cheap platforms for TFLite Micro, with S3 versions and optional PSRAM for larger models.[^2_8] - Generic ESP32‑S3 dev boards with Wi‑Fi/BT and a few MB PSRAM are often in the 5–10 USD range on marketplaces. - Nano‑class ML boards: - Official Arduino Nano 33 BLE and TinyML kits are more expensive (~20–70 USD retail), but they bundle strong MCUs and rich sensors for on‑device ML and offline tasks.[^2_9][^2_10][^2_11][^2_12] - Behavior: - On an ESP32‑S3‑class part you can run a small quantized model plus a hand‑rolled agent loop (finite‑state + simple planner), keep everything offline, and still respond to multiple sensor streams or do basic language/command parsing locally.[^2_2][^2_13] So for something recognizably “agent‑like,” capable of multiple skills but still disconnected from the network, a realistic lower bound in hobbyist quantities is around 5–10 USD for the compute board, plus whatever sensors/actuators you bolt on. ## If you want to push the limit - Absolute BOM minimization: projects like “1 Dollar TinyML” show that with aggressive part selection (e.g., CH32V003 / PY32F0‑class MCUs at 0.10–0.20 USD, sub‑0.30 USD accelerometers, ultra‑cheap BLE) you can fit MCU + sensor(s) + radio under 1 USD BOM at ~200 units, leaving just enough flash/RAM for a tiny learned policy loop.[^2_4][^2_5] - “General” via networked specialization: at that extreme, “general task pursuit” can be offloaded to a *swarm*—each node does one thing cheaply (detect X, count Y), and higher‑level behavior emerges from the network policy rather than a big model per node. If you tell me what “general task” you have in mind (e.g., “be a room‑monitoring butler,” “patrol a garden and look for anomalies,” “do on‑device summarization of sensor streams”), I can sketch a concrete design that hits the absolute lowest cost envelope you’re comfortable with. <span style="display:none">[^2_14][^2_15]</span> <div align="center">⁂</div> [^2_1]: https://hdsr.mitpress.mit.edu/pub/0gbwdele [^2_2]: https://www.dfrobot.com/blog-13921.html [^2_3]: https://www.rs-online.com/designspark/tinyml-ai-for-microcontrollers [^2_4]: https://hackaday.io/project/194511-1-dollar-tinyml [^2_5]: https://hackaday.com/2024/05/02/1-tinyml-board-for-your-ai-sensor-swarm/ [^2_6]: https://www.ignitec.com/insights/tiny-machine-learning-tinyml-in-the-wild-offline-environmental-ai/ [^2_7]: https://www.seeedstudio.com/blog/2021/06/14/everything-about-tinyml-basics-courses-projects-more/ [^2_8]: https://www.reddit.com/r/microcontrollers/comments/1dvxyq5/cheapest_microcontroller_to_play_around_with/ [^2_9]: https://store-usa.arduino.cc/collections/arduino/products/arduino-tiny-machine-learning-kit [^2_10]: https://www.aliexpress.com/item/1005009148245489.html [^2_11]: https://shop.stemsmartlabs.com/shop/original-arduino-nano-33-ble-sense-tiny-machine-learning-kit/ [^2_12]: https://store-usa.arduino.cc/products/arduino-nano-33-ble [^2_13]: https://blog.meetneura.ai/tinyml-microcontroller-ml/ [^2_14]: https://www.aliexpress.com/item/1005009218183817.html [^2_15]: https://store-usa.arduino.cc/products/nano-33-ble-rev2 --- # basic bootstrapping stuff like write code or perform online finetuning could make sense i guess what's the cheapest people are doing fpga language models? People *are* running language‑model‑ish things on FPGAs, but the price floor for something you’d recognize as an LLM (even tiny) is still “cheap dev board” money, not “\$5 MCU” money; think roughly 6–50 USD for hobby‑grade hardware, with serious LLM‑on‑FPGA work mostly on much pricier parts.[^3_1][^3_2][^3_3][^3_4] ## What “FPGA LLM” usually means today - Most open discussions are about **Llama‑class models on mid/high‑end Xilinx boards or PCIe cards**, not the \$10 iCE40 tier.[^3_3][^3_5] - Academic/industrial projects (e.g. *Hummingbird*, *SpeedLLM*) target **embedded or cloud FPGAs but still with decent on‑chip/attached DRAM**, optimized for TinyLlama‑scale models; these are not toy dev boards.[^3_4][^3_5] - Commercial offerings (like an FPGA LLM inference server discussed in /r/LocalLLaMA) are rackmount‑class systems in the **hundreds of thousands of dollars**, so not relevant for “cheapest.”[^3_6][^3_7] So “cheapest” in practice currently means “smallest dev board that still has enough LUTs and external RAM to host a reasonable accelerator plus weights.” ## Cheapest classes of boards people use >From cheap‑FPGA catalogs and community threads:[^3_2][^3_8][^3_1] | Board / family | Approx price | Suitability for LLM‑ish work | | :-- | :-- | :-- | | Sipeed Tang Nano (GW1N‑1) | ≈6 USD | Very small (≈1k LUT4 + 8 MB PSRAM); good for toy matmul engines or tiny RNNs/transformer blocks, but not a full LLaMA without extreme compression/streaming.[^3_2] | | Cheap iCE40 / HX1K / small Gowin boards | 20–40 USD | Enough for minimal accelerators or “TinyML on FPGA” experiments, but still very tight for full LLM graphs.[^3_2][^3_9] | | Zynq 7010/7020 boards (PYNQ‑Z2, Sipeed Tang Hex, etc.) | 70–120+ USD | Popular “entry” ML boards: ARM core + FPGA fabric + DRAM; can offload transformer kernels to the PL while managing memory and control in the PS.[^3_1][^3_2] | | Larger open FPGA boards (ULX3S, etc.) | 100+ USD | Comfortable for custom accelerators and experimentation, but no longer “cheapest possible.”[^3_2] | At the very bottom, something like a **Tang Nano (~6 USD) with 8 MB PSRAM** is about as low as you’ll see for “I want to implement a transformer core on FPGA at all,” but you’re then doing *extreme* model shrinking (sub‑million‑parameter models, aggressive quantization, streaming weights from off‑chip).[^3_2] ## Cheapest realistic path to “FPGA LLM” If your goal is “offline agent that can write code / do basic LLM‑like reasoning” rather than just “I did a transformer kernel on an FPGA,” the cheapest *practical* setups people actually use look like: - **Hybrid: ARM (or RISC‑V) + FPGA dev board**, where: - CPU (Zynq PS, or external SBC like a Pi) runs the agent loop, tokenizer, sampling, etc. - FPGA hosts one or more matmul/attention/MLP accelerators accessed via AXI/PCIe.[^3_1][^3_3][^3_4] - **Boards**: lowest you can go and still be in that game is typically **Zynq‑7010/7020‑class boards around 70–120 USD** (PYNQ‑Z2, Sipeed Tang Hex, MiniZed, etc.), which bring enough fabric and DRAM to do something recognizably “LLM,” especially with TinyLlama‑scale models.[^3_5][^3_4][^3_1][^3_2] Academic Hummingbird‑style designs explicitly pitch themselves as “affordable LLM on embedded FPGA,” but the underlying parts are still **Spartan‑UltraScale/embedded‑class**, i.e., OEM rather than \$10 hobby boards.[^3_4] ## How low could you push it? If you’re willing to accept: - Very small models (tiny transformers, maybe tens/hundreds of k parameters). - Extremely slow throughput (seconds per token). - Strong external‑host help (e.g., a Pi or RISC‑V SBC managing weights in DRAM and just using the FPGA as a MAC array). …then in principle you could: - Use a **Tang Nano (~6 USD) or similar GOWIN board with 8 MB PSRAM** as a “language‑core accelerator” and hang it off a cheap SBC/MCU via SPI or parallel.[^3_2] - Keep weights in SBC DRAM, stream tiles into the FPGA, and implement just enough attention/MLP to say “yes, this is a transformer,” not “this is a competitive LLM.” Nobody’s really packaging *that* as a turnkey “FPGA LLM” yet in public projects; most public work either lives on **bigger Zynq/Alveo boards or in industry boxes**.[^3_3][^3_5][^3_1][^3_4] If you tell me your target envelope (e.g. “sub‑\$20 BOM and I’m okay with toy‑scale transformers that can just do bootstrapping primitives like code completion for HDL”), I can sketch a concrete architecture that partitions work between a tiny FPGA and a cheap host. <span style="display:none">[^3_10][^3_11][^3_12][^3_13][^3_14][^3_15]</span> <div align="center">⁂</div> [^3_1]: https://www.reddit.com/r/FPGA/comments/1l3si4q/fpga_development_board_recommendations_for_ml/ [^3_2]: https://www.joelw.id.au/FPGA/CheapFPGADevelopmentBoards [^3_3]: https://www.reddit.com/r/FPGA/comments/18j4o70/is_it_possible_to_run_llama_inference_on_an_fpga/ [^3_4]: https://arxiv.org/html/2507.03308v1 [^3_5]: https://arxiv.org/html/2507.14139v1 [^3_6]: https://www.reddit.com/r/LocalLLaMA/comments/1ilt4r7/fpga_llm_inference_server_with_super_efficient/ [^3_7]: https://intuitionlabs.ai/articles/llm-inference-hardware-enterprise-guide [^3_8]: https://digilent.com/shop/products/fpga-boards/introductory-fpga-boards/ [^3_9]: https://www.reddit.com/r/FPGA/comments/1bsd5uz/tinyml_on_fpga/ [^3_10]: https://digilent.com/shop/products/fpga-boards/ [^3_11]: https://www.sparkfun.com/development-boards/fpga.html [^3_12]: https://www.youtube.com/watch?v=W_P8rSKs4Co [^3_13]: https://hackaday.io/project/194511-1-dollar-tinyml [^3_14]: https://hackaday.com/2024/05/02/1-tinyml-board-for-your-ai-sensor-swarm/ [^3_15]: https://www.reddit.com/r/LocalLLaMA/comments/1r90vlx/models_for_fpga_coding/ --- # who's redefining the multilayer transformer architecture to solve the hobbyists-have-cheap-AI problem? There isn’t a single “one person” redefining the transformer for cheap‑hobbyist AI, but there’s a pretty clear *ecosystem* of people and lines of work trying to solve that exact problem from different angles: new architectures, sparsity/quantization tricks, and hardware‑codesign for tiny/edge devices.[^4_1][^4_2][^4_3][^4_4][^4_5] ## People and groups attacking “cheap AI for hobbyists” - **Edge‑efficient sequence‑model theorists (SSMs instead of transformers).** - The **Structured State Space Models (S4)** line, led by Albert Gu and collaborators, replaces attention layers with carefully parameterized state‑space layers that can model long sequences with much better compute/memory scaling, potentially suiting low‑resource hardware.[^4_6][^4_7][^4_8] - This work is explicitly about “efficient, principled sequence models” that could be a backbone for cheap, long‑context models without full transformer overhead.[^4_7][^4_6] - **“Tiny devices” transformer people.** - Papers like **“Efficient Sparse Transformer Design and Deployment on Tiny Devices”** focus on structured sparsity, low‑rank approximations, and quantization to fit transformers on MCUs and other tiny edge hardware.[^4_4] - Surveys on **AI edge devices and lightweight CNN/LLM models** catalog many such techniques (parameter sharing, pruning, mixed‑precision) aimed at running models on low‑power, low‑cost boards.[^4_1] - **Small‑LLM‑for‑edge model designers.** - Commercial/open groups building models like **TinyLlama, Gemma 2B, small Qwen/GLM variants** explicitly target edge deployment on ARM CPUs or similar, trading depth/width and vocabulary tricks for better inference cost.[^4_3][^4_5] - These teams aren’t usually re‑inventing the entire transformer, but they’re heavily tweaking depth, attention patterns, RoPE/ALiBi variants, etc., specifically so models run decently on modest hardware.[^4_5][^4_3] - **FPGA LLM accelerator folks (architecture co‑design).** - Work like **Hummingbird**, an LLM accelerator for embedded FPGAs, doesn’t change transformer math at a high level but radically reshapes how it’s realized: DSP‑efficient GEMV cores, aggressive weight offloading, and layout changes allow larger models (e.g. LLaMA3‑8B) on cost‑optimized Spartan UltraScale‑class FPGAs.[^4_2][^4_9] - Their focus is *“support stronger LLMs and longer contexts on much smaller FPGAs”*, which is absolutely aligned with “cheap hardware + serious models,” even if still above pure hobbyist budgets.[^4_9][^4_2] ## Architectural ideas that matter for hobbyists If you’re thinking “who is changing multilayer transformer structure so I can run agents on \$10–\$50 hardware,” the key levers people are collectively pulling are: - **Replacing full attention with cheaper sequence modules** - S4/SSMs and related work trade quadratic attention for linear‑time/state‑space operations, while keeping long‑range modeling ability.[^4_8][^4_6][^4_7] - **Making attention itself cheaper on tiny devices** - Sparse/k‑NN/low‑rank attention, block‑sparse patterns, and **efficient sparse transformer designs for tiny MCUs** specifically target low‑SRAM, low‑bandwidth setups.[^4_4] - **Shrinking and specializing LLMs for edge** - Designers of **TinyLlama, Gemma 2B, and other “small LLMs for edge devices”** pick parameter counts, layer shapes, and vocab schemes to fit in limited RAM and run at acceptable latency on CPUs/edge accelerators.[^4_3][^4_5] - **Hardware‑aware layer design (especially on FPGAs)** - Projects like **Hummingbird** co‑design the block structure (GEMV‑centric compute, weight streaming, layout) with embedded FPGAs’ DSP/LUT/BRAM characteristics, enabling LLM inference on smaller, cheaper chips than earlier accelerators.[^4_2][^4_9] ## How you could plug into this as a hobbyist - Track and experiment with **SSM/S4‑style architectures** as drop‑in replacements for parts of a transformer stack, then try compiling them down to microcontroller or FPGA back‑ends. The math is explicitly aimed at efficient implementations.[^4_6][^4_7][^4_8] - Follow “**efficient sparse transformer on tiny devices**” work and prototype those kernels on the cheapest hardware you can stand (ESP32‑S3, STM32 with external PSRAM, small FPGAs) to see what breaks first: memory, bandwidth, or control.[^4_1][^4_4] - Take **small‑LLM‑for‑edge** designs (TinyLlama, Gemma‑2B‑style) as *architectural templates* and aggressively downscale/quantize them, then measure what happens when you target RISC‑V, cheap ARM SBCs, or small FPGAs.[^4_5][^4_3] - Use the **Hummingbird‑style co‑design mindset** even on much cheaper FPGAs: start from a simple GEMV/attention core tuned to your DSP/BRAM budget, then design the “transformer” around *that*, rather than porting a fixed big‑model spec.[^4_9][^4_2] If you tell me your preferred target (MCU with no OS, Pi‑class ARM, low‑end FPGA) I can suggest a concrete architecture recipe and where SSM vs sparse‑transformer vs “tiny LLM” style makes the most sense to prototype. <span style="display:none">[^4_10][^4_11][^4_12][^4_13][^4_14][^4_15]</span> <div align="center">⁂</div> [^4_1]: https://www.sciencedirect.com/science/article/abs/pii/S0925231224015625 [^4_2]: https://arxiv.org/pdf/2507.03308.pdf [^4_3]: https://www.siliconflow.com/articles/en/best-small-llms-for-edge-devices [^4_4]: https://arxiv.org/html/2311.01759v2 [^4_5]: https://www.edge-ai-vision.com/2025/01/harnessing-the-power-of-llm-models-on-arm-cpus-for-edge-devices/ [^4_6]: https://arxiv.org/abs/2111.00396 [^4_7]: https://purl.stanford.edu/mb976vf9362 [^4_8]: https://openreview.net/pdf?id=uYLFoz1vlAC [^4_9]: https://arxiv.org/html/2507.03308v1 [^4_10]: https://www.subian-electric.com/blog/efficiency-small-transformers-modern-energy-solutions/ [^4_11]: https://finance.yahoo.com/news/data-center-transformers-business-analysis-110300962.html [^4_12]: https://www.bloomberg.com/features/2025-bottlenecks-transformers/ [^4_13]: https://northfieldtransformers.com/blog/data-center-expansion-reshaping-transformer-demand/ [^4_14]: https://www.linkedin.com/pulse/small-power-transformer-below-220kv-market-2025-euusf [^4_15]: https://www.gminsights.com/industry-analysis/transformer-optimized-ai-chip-market
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