Pleased to announce the successful completion of a tiny high-level computing language for high-speed, low-power, embedded computing on bare silicon (no BIOS, no OS). In terms of size, cost, and carbon footprint, the kernel clocks in at 730 bytes which includes a fully extensible runtime kernel providing DSL (domain specific language) capability for application specific computing.
What has been achieved:
- 372 byte machine coded kernel (yes, this is bytes, not KB, MB or GB). This is small enough to fit into the smallest ROMs, while still leaving plenty of space for application RAM.
- A further 358 bytes comprise bootstrapped runtime instructions written in the language itself, and data organized in jump-tables for efficient threaded execution.
- 40 kernel level instructions provide the full expressibility of C for application code.
- Compressed application code runs through a threaded design providing high speed code execution (as fast or faster than C) using native code.
- The core kernel provides a codestream reader that provides Domain specific language (DSL) capability to allow first class language expansion with the full capability of a Forth or a Lisp. This means expanded instructions have the same first-class attributes as the core kernel instructions.
- Language is based on a simple stack-based virtual machine universal architecture onto which any chip can be mapped. Proof of concept designs with Arduino (Atmel 328), ATTiny85, and x86 chips.
- Portability between chips is achieved through a light-weight mapping layer to the 40 kernel instructions without requiring any other layer. A single afternoon mapping the 40 kernel instructions between chips and adapting the kernel code is all that is required to port to a new chip.
- Lightweight development stack (open source) with compiler/assembler/debugger for cross-compilation.
- User/Programmer interface is via live serial link, via keyboard/screen, or via binary ROM/RAM load.
More details to follow.
TinyPhoto is a small rotating photobook embedded graphics project that uses the low-power ATtiny85 microcontroller (3mA) and a 128×64 pixel OLED display (c.5-10mA typical, 15mA max). This combination can deliver at least 20 hrs of continuous play on a 3V coin cell battery (225mAh capacity). TinyPhoto can be readily built from a handful of through-hole electronic components (12 parts, £5) organized to fit onto a 3cm x 7cm single-sided prototype PCB. The embedded software is c.150 lines of C code and uses less than 1,300 bytes of on-chip memory. TinyPhoto rotates through five user-selectable images using a total of 4,900 bytes (yes, bytes!) stored in the on-chip flash RAM. The setup produces crisp photos on the OLED display with a real-time display rate that is instantaneous to the human eye with the Tiny85 boosted to run at 8MHz. A custom device driver (200 bytes) sets up the OLED screen and enables pixel-by-pixel display. Custom Forth code converts a 0-1 color depth image into a byte-stream that can be written to the onboard flash for rapid display. It is a reminder of what can be accomplished with low-fat computing…
The magic, of course, is in the software. This article describes how this was done, and the software that enables it. Checkout the TinyPhoto review on Hackaday!
 Tiny Photo – 3cm x 7cm photo viewer powered by ATTiny85 8-bit microcontroller sending pixel level image data to OLED display (128×64 pixels), powered by 3V coin cell battery. Cycles through 5 images stored in 5kB of on-chip Flash RAM. (Note, this is 1 million times less memory than on a Windows PC with 8GB RAM). The magic is in the software. Continue reading this article…
Voice controlled hardware requires four capabilities: (1) vocal response to trigger events (sensors/calculations-to-brain), (2) speech generation (brain-to-mouth), (3) speech recognition (ear-to-brain), and (4) speech understanding (brain-to-database, aka learning). These capabilities can increasingly be implemented using off-the-shelf modules, due to progress in advanced low-cost silicon capable of digital signal processing (DSP) and statistical learning/machine learning/AI.
In this article we look at the value chain involved in building voice control into hardware. We cover highlights in the history of artificial speech. And we show how to convert an ordinary sensor into a talking sensor for less than £5. We demonstrate this by building a Talking Passive Infra-Red (PIR) motion sensor deployed as part of an April Fool’s Day prank (jump to the design video and demonstration video).
The same design pattern can be used to create any talking sensor, with applications abounding around home, school, work, shop, factory, industrial site, mass-transit, public space, or interactive art/engineering/museum display.
 Bringing Junk Model Robots to life with Talking Motion Sensors (April Fools Prank, 2021) Continue reading this article…
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