The Apple Silicon That Never Was

June 3, 2023 by Bryan Cockfield 22 Comments

Over Apple’s decades-long history, they have been quick to adapt to new processor technology when they see an opportunity. Their switch from PowerPC to Intel in the early 2000s made Apple machines more accessible to the wider PC world who was already accustomed to using x86 processors, and a decade earlier they moved from Motorola 68000 processors to take advantage of the scalability, power-per-watt, and performance of the PowerPC platform. They’ve recently made the switch to their own in-house silicon, but, as reported by [The Chip Letter], this wasn’t the first time they attempted to design their own chips from the ground up rather than using chips from other companies like Motorola or Intel.

In the mid 1980s, Apple was already looking to move away from the Motorola 68000 for performance reasons, and part of the reason it took so long to make the switch is that in the intervening years they launched Project Aquarius to attempt to design their own silicon. As the article linked above explains, they needed a large amount of computing power to get this done and purchased a Cray X-MP/48 supercomputer to help, as well as assigning a large number of engineers and designers to see the project through to the finish. A critical error was made, though, when they decided to build their design around a stack architecture rather than a RISC. Eventually they switched to a RISC design, though, but the project still had struggled to ever get a prototype working. Eventually the entire project was scrapped and the company eventually moved on to PowerPC, but not without a tremendous loss of time and money.

Interestingly enough, another team were designing their own architecture at about the same time and ended up creating what would eventually become the modern day ARM architecture, which Apple was involved with and currently licenses to build their M1 and M2 chips as well as their mobile processors. It was only by accident that Apple didn’t decide on a RISC design in time for their personal computers. The computing world might look a lot different today if Apple hadn’t languished in the early 00s as the ultimate result of their failure to develop a competitive system in the mid 80s. Apple’s distance from PowerPC now doesn’t mean that architecture has been completely abandoned, though.

Thanks to [Stephen] for the tip!

ADATA SSD Gets Liquid Cooling, But Not Everyone’s Convinced

June 3, 2023 by Lewin Day 19 Comments

Solid-state drives (SSDs) were a step change in performance when it came to computer storage. They offered incredibly fast seek times by virtue of dispensing with solid rust for silicon instead. Now, some companies have started pushing the limits to the extent that their drives supposedly need liquid cooling, as reported by The Register.

The device in question is the ADATA Project NeonStorm, which pairs a PCIe 5.0 SSD with RGB LEDs, a liquid cooling reservoir and radiator, and a cooling fan. The company is light on details, but it’s clearly excited about its storage products becoming the latest piece of high-end gamer jewelry.

Notably though, not everyone’s jumping on the bandwagon. Speaking to The Register, Jon Tanguy from Crucial indicated that while the company has noted modern SSDs running hotter, it doesn’t yet see a need for active cooling. In their case, heatsinks have proven enough. He notes that NAND flash used in SSDs actually operates best at 60 to 70 C. However, going beyond 80 C risks damage and most drives will shutdown or throttle access at this point.

Realistically, you probably don’t need to liquid cool your SSDs, even if you’ve got the latest and greatest models. However, if you want the most tricked out gaming machine on Twitch, there’s plenty of products out there that will happily separate you from your money.

Software Driving Hardware

June 3, 2023 by Elliot Williams 69 Comments

We were talking about [Christopher Barnatt]’s very insightful analysis of what the future holds for the Raspberry Pi single board computers on the Podcast. On the one hand, they’re becoming such competent computers that they are beginning to compete with lightweight desktop machines, instead of just being a hacker curiosity.

On the other hand, especially given the shortage and the increase in price that has come with the Pi’s expanding memory endowments, a lot of people who would “just throw in a Raspberry Pi” are starting to think more carefully about their options. Five years ago, this would have meant looking into what you could whip together on an Arduino-based platform, either on actual Arduino hardware or on an ESP8266 or similar, but that’s a very different beast from a programmer’s perspective. Working with microcontrollers used to be very different from working with even the smallest Linux machines.

These days, there is no shortage of microcontrollers that have enough memory – both flash and RAM – to support a higher-level environment like MicroPython. And if you think about it, MicroPython brings to the microcontrollers a lot of what people were using a Raspberry Pi for in projects anyway: a friendly interactive programming environment that was free of the compile-here, flash-there debug cycle. If you’re happy coding Python on a single-board Linux computer, you’ll be more or less happy coding in MicroPython or Circuit Python on a microcontroller.

And what this leaves us with, as hackers, is a fantastic spectrum of choices. Where before there was a hard edge between programming C on an 8-bit PIC or an AVR and working with something that had a full Linux operating system like a Pi, it’s all blurry now. And as the Pis, the Jetson, and all the other Linux SBCs are blurring the boundary with more traditional computers as they all become more competent and gain more computer-like peripherals. Nowadays your choice is much freer, and the hardware landscape more fluid. You don’t have to let software development concerns drive your hardware choices, and we think that’s a great thing.

Zelda Guardian Sculpture Tracks Humans And Pets Via Camera

June 3, 2023 by Lewin Day 3 Comments

In The Legend of Zelda: Breath of the Wild Guardians are a primitive form of sentry turret that tracks the player with a watchful eye. Inspired by this, [npentrel] decided to whip up one of her own in the real world.

The build relies on a Raspberry Pi kitted out with its usual camera for machine vision purposes. It uses the Viam robot toolkit, which runs a machine learning model to detect pets and humans on the camera feed. The guardian then tracks any pets or humans that show up by turning its head, and thus the camera, with a servo controlled by a PWM signal via the Raspberry Pi’s GPIO pins. It’s all wrapped up in a nicely-decorated 3D printed model that really does look like something straight out of Breath of the Wild.

Sentry projects are a great way to learn about electronics, mechanics, and image processing techniques. It’s funny to see how advanced and complicated these projects were fifteen years ago, compared to how easy they are today with modern machine learning libraries. How times change!

A Fresnel Lens Without The Pain

June 3, 2023 by Jenny List 21 Comments

Making a traditional glass lens requires a lot of experience, skill, and patience grinding a piece of glass to the required shape, and is not for the casual experimenter. Making a glass Fresnel lens with its concentric rings requires even more work, but as the ever-resourceful [Robert Murray-Smith] shows us, a Fresnel lens can be made from far more mundane materials. He shows us a working lens made from transparent plastic tube, and even successfully smoulders a piece of paper with it under the anaemic British sun.

His lens, with its circular profile tube filled with water, is not perhaps the most efficient lens in terms of light focused per unit area of lens. From dredging up our highschool physics lessons we are guessing that half the light is diffracted outwards rather than inwards by the cylindrical profile of the coil, but for the cost of the whole device we’re not sure that matters. Next time we’re shipwrecked on a desolate island with a handy supply of clear plastic tube and fresh water, we know we can always raise a fire.

If Fresnel lenses interest you, we’ve taken a look in the past at their history.

Continue reading “A Fresnel Lens Without The Pain” →

C64 Gets ChatGPT Access Via BBS

June 2, 2023 by Lewin Day 4 Comments

ChatGPT, powered by GPT 3.5 and GPT 4, has become one of the most popular Large Language Models (LLM), due to its ability to hold passable conversations and generate large tracts of text. Now, that very tool is available on the Commodore 64 via the Internet.

Obviously, a 6502 CPU with just 64 kilobytes of RAM can barely remember a dictionary, let alone the work with something as complicated as a modern large language model. Nor is the world’s best-selling computer well-equipped to connect to modern online APIs. Instead, the C64 can access ChatGPT through the Retrocampus BBS, as demonstrated by [Retro Tech or Die].

Due to security reasons, the ChatGPT area of the BBS is only available to the board’s Patreon members. Once in, though, you’re granted a prompt with ChatGPT displayed in glorious PETSCII on the Commodore 64. It’s all handled via a computer running as a go-between for the BBS clients and OpenAI’s ChatGPT service, set up by board manager [Francesco Sblendorio]. It’s particularly great to see ChatGPT spitting out C64-compatible BASIC.

While this is a fun use of ChatGPT, be wary of using it for certain tasks in wider society. Video after the break.

Continue reading “C64 Gets ChatGPT Access Via BBS” →

Overall design of retina-inspired NB perovskite PD for panchromatic imaging. (Credit: Yuchen Hou et al., 2023)

Perovskite Sensor Array Emulates Human Retina For Panchromatic Imaging

June 2, 2023 by Maya Posch 13 Comments

The mammalian retina is a complex system consisting out of cones (for color) and rods (for peripheral monochrome) that provide the raw image data which is then processed into successive layers of neurons before this preprocessed data is sent via the optical nerve to the brain’s visual cortex. In order to emulate this system as closely as possible, researchers at Penn State University have created a system that uses perovskite (methylammonium lead bromide, MAPbX₃) RGB photodetectors and a neuromorphic processing algorithm that performs similar processing as the biological retina.

Panchromatic imaging is defined as being ‘sensitive to light of all colors in the visible spectrum’, which in imaging means enhancing the monochromatic (e.g. RGB) channels using panchromatic (intensity, not frequency) data. For the retina this means that the incoming light is not merely used to determine the separate colors, but also the intensity, which is what underlies the wide dynamic range of the Mark I eyeball. In this experiment, layers of these MAPbX₃ (X being Cl, Br, I or combination thereof) perovskites formed stacked RGB sensors.

The output of these sensor layers was then processed in a pretrained convolutional neural network, to generate the final, panchromatic image which could then be used for a wide range of purposes. Some applications noted by the researchers include new types of digital cameras, as well as artificial retinas, limited mostly by how well the perovskite layers scale in resolution, and their longevity, which is a long-standing issue with perovskites. Another possibility raised is that of powering at least part of the system using the energy collected by the perovskite layers, akin to proposed perovskite-based solar panels.

(Heading: Overall design of retina-inspired NB perovskite PD for panchromatic imaging. (Credit: Yuchen Hou et al., 2023) )