{"id":367,"date":"2026-02-13T10:56:03","date_gmt":"2026-02-13T18:56:03","guid":{"rendered":"https:\/\/scienceblog.com\/sciencechina\/?p=367"},"modified":"2026-02-13T10:56:03","modified_gmt":"2026-02-13T18:56:03","slug":"the-ancient-abacus-trick-that-could-supercharge-ai","status":"publish","type":"post","link":"https:\/\/scienceblog.com\/sciencechina\/2026\/02\/13\/the-ancient-abacus-trick-that-could-supercharge-ai\/","title":{"rendered":"The Ancient Abacus Trick That Could Supercharge AI"},"content":{"rendered":"

The Chinese suanpan has been around for roughly 1,000 years. Slide a bead up, slide a bead down \u2014 each rod operates independently, and the number it represents depends only on where the beads sit. Break one rod and the rest carry on just fine. It is, in a sense, the world’s most fault-tolerant calculator.<\/p>\n

Now a team at Tsinghua University in Beijing has borrowed that logic for a photonic computing chip, and the result could help solve one of the gnarliest bottlenecks facing AI hardware. Their architecture, also called SUANPAN, replaces the beads with pairs of tiny lasers and light detectors, each one working on its own little piece of a calculation without needing to know what any of its neighbours are doing.<\/p>\n

The problem they’re tackling is scalability \u2014 or the lack of it. Most photonic computing designs rely on beams of light interacting with one another, bouncing through beam splitters, diffraction gratings, and arrays of liquid crystal cells to perform the matrix multiplications that underpin modern AI. That approach works, but there’s a catch: because the light beams are coupled together, adding more components doesn’t simply give you more computing power. Errors compound. Losses pile up. You hit a ceiling pretty quickly.<\/p>\n

Yidong Huang and colleagues at Tsinghua, working with collaborators at Peking University and Berxel Photonics, took a different tack. Instead of forcing light beams to interact, they kept them apart entirely. Each emitter-detector pair \u2014 they call these BEADs, short for Bit Encoding and Analog Detecting \u2014 handles the multiplication of two numbers on its own. One number gets encoded as the brightness of a laser; the other is represented by whether the detector is switched on or off. The photocurrent that comes out is proportional to the product of the two. Wire all the detectors together and, thanks to Kirchhoff’s current law, you get the sum of all those products. That’s a vector inner product \u2014 the bread-and-butter operation behind neural networks and optimisation algorithms.<\/p>\n

What makes this clever, perhaps even a bit elegant, is the encoding scheme. Conventional photonic systems need banks of digital-to-analogue converters to translate electronic data into light signals, and those converters are power-hungry and bulky. The SUANPAN sidesteps the issue by encoding digital information across multiple BEADs in binary, the same way an abacus uses bead positions. Each BEAD only ever needs to be on or off \u2014 1-bit information \u2014 so no converter is required on the input side. On the output, a single analogue-to-digital converter reads the total photocurrent. It’s a tidy trick.<\/p>\n

For their proof-of-concept, the researchers built a chip with a grid of 64 vertical-cavity surface-emitting lasers (VCSELs) paired with 64 photodetectors made from MoTe\u2082, a two-dimensional material just a few atoms thick. The VCSELs emit at 850 nanometres; the MoTe\u2082 detectors can have their sensitivity tuned by adjusting the bias voltage, which is how the team sets different binary weights for different bits.<\/p>\n

The results were solid. Random vector inner products came back with fidelities above 98 percent across 2-bit, 4-bit, and 8-bit precisions, and stayed above 95% as the dimensionality increased. The team then reconfigured the same hardware \u2014 no physical changes, just reprogramming which BEADs were on or off \u2014 to tackle two standard AI benchmarks. A randomly generated 1024-dimensional Ising optimisation problem was successfully solved, which is apparently the highest dimensionality any optical Ising machine using a heuristic algorithm has managed. And a neural network running on the SUANPAN recognised handwritten digits from the MNIST dataset with 88 percent accuracy, close to the 90 percent the same network achieved running on a conventional computer.<\/p>\n

Neither figure will knock your socks off on its own. But the point isn’t raw performance \u2014 it is that the architecture did all of this with independent units that could, in principle, be scaled up by just stamping out more laser-detector pairs. The team reckon that with emitters and detectors smaller than 10 micrometres and bandwidths above 1 gigahertz \u2014 both achievable with existing technology \u2014 a single chip could reach computing speeds above 1 peta-operation per second for 1-bit quantisation. For context, that’s roughly in the territory where optical computing starts to look competitive with electronic accelerators for certain workloads.<\/p>\n

There are caveats, of course. The current prototype is modest: 64 BEADs, with laser and detector arrays sitting about 1.5 metres apart, connected by a zoom lens. Integrating the emitters and detectors onto the same chip would slash propagation delays and boost efficiency enormously \u2014 at the moment, most of the light from each laser misses the detector channel entirely, because the spot size is around 200 micrometres wide while the detector channel is only about 10 micrometres across. That’s a lot of wasted photons. The MoTe\u2082 detectors also degraded over three months of testing, which knocked the accuracy of a two-layer neural network down to 84 percent.<\/p>\n

Still, the underlying idea has a certain stubbornness to it that makes it hard to dismiss. Because the BEADs don’t talk to each other optically, a dead unit doesn’t poison the rest; you just lose one dimension. No phase correction is needed, either, which removes one of the fiddliest engineering headaches in photonic computing. And the whole thing can be reconfigured in software for different tasks \u2014 Ising problems one moment, neural networks the next \u2014 without touching the hardware.<\/p>\n

We are probably still some years from seeing photonic processors like this inside data centres. But if AI’s appetite for matrix maths keeps growing at its current pace, and there is no sign of it slowing, then an architecture that scales the way transistors once did \u2014 by simply making more copies of the same unit \u2014 might be exactly what the field needs. The abacus, it turns out, had the right idea all along.<\/p>\n

Study link: https:\/\/www.nature.com\/articles\/s41377-025-02059-7<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The Chinese suanpan has been around for roughly 1,000 years. Slide a bead up, slide a bead down \u2014 each rod operates independently, and the number it represents depends only on where the beads sit. Break one rod and the rest carry on just fine. It is, in a sense, the world’s most fault-tolerant calculator. … Read more<\/a><\/p>\n","protected":false},"author":1299,"featured_media":371,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","_links_to":"","_links_to_target":""},"categories":[5,2],"tags":[],"class_list":["post-367","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-physics-mathematics","category-technology","generate-columns","tablet-grid-50","mobile-grid-100","grid-parent","grid-50"],"yoast_head":"\nThe Ancient Abacus Trick That Could Supercharge AI - SciChi<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/scienceblog.com\/sciencechina\/2026\/02\/13\/the-ancient-abacus-trick-that-could-supercharge-ai\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Ancient Abacus Trick That Could Supercharge AI\" \/>\n<meta property=\"og:description\" content=\"The Chinese suanpan has been around for roughly 1,000 years. 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Now, Chinese researchers have developed an ingenious solution: flexible robotic rods equipped with self-adjusting dampeners that can stabilize tumbling space debris while suppressing their own violent shaking. The system combines piezoelectric\u2026","rel":"","context":"In "Physics & Mathematics"","block_context":{"text":"Physics & Mathematics","link":"https:\/\/scienceblog.com\/sciencechina\/category\/physics-mathematics\/"},"img":{"alt_text":"satellite in space","src":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/07\/satellite-67718_1280.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/07\/satellite-67718_1280.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/07\/satellite-67718_1280.jpg?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/07\/satellite-67718_1280.jpg?resize=700%2C400&ssl=1 2x"},"classes":[]},{"id":47,"url":"https:\/\/scienceblog.com\/sciencechina\/2025\/04\/22\/new-ai-drones-spot-victims-through-forest-darkness\/","url_meta":{"origin":367,"position":2},"title":"New AI Drones Spot Victims Through Forest, Darkness","author":"SciChi","date":"April 22, 2025","format":false,"excerpt":"Finding survivors in wilderness after disasters has long been a race against time, with rescue teams battling challenging terrain and weather conditions. Now, researchers have developed an artificial intelligence system that dramatically improves how drones detect people during search and rescue missions\u2014even in dense forests or darkness. A team from\u2026","rel":"","context":"In "Technology"","block_context":{"text":"Technology","link":"https:\/\/scienceblog.com\/sciencechina\/category\/technology\/"},"img":{"alt_text":"The illustrated scenarios are based on locations in Xinyang and Huizhou, China. The system collected samples that included both RGB and infrared spectral bands, which were then used to construct the VTSaR dataset.","src":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/04\/remotesensing.0474.fig_.006.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/04\/remotesensing.0474.fig_.006.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/04\/remotesensing.0474.fig_.006.jpg?resize=525%2C300&ssl=1 1.5x"},"classes":[]},{"id":406,"url":"https:\/\/scienceblog.com\/sciencechina\/2026\/03\/31\/a-catalyst-that-heats-itself-up-can-turn-sunlight-and-co2-into-fuel\/","url_meta":{"origin":367,"position":3},"title":"A Catalyst That Heats Itself Up Can Turn Sunlight and CO2 into Fuel","author":"ScienceBlog.com","date":"March 31, 2026","format":false,"excerpt":"Light hits a particle of indium oxide, and something unusual happens. The palladium clusters dotting its surface don't just absorb the photons. They convert them into heat, raising the catalyst's skin temperature to around 230 degrees Celsius within seconds, even though the source of illumination is nothing more exotic than\u2026","rel":"","context":"Similar post","block_context":{"text":"Similar post","link":""},"img":{"alt_text":"Both Pd single atoms (Pd1) and clusters (Pdc) were constructed in three-dimensional ordered macroporous (3DOM) In2O3 for photocatalytic CO2 reduction with H2O. The large surface area and abundant pore channels of 3DOM-In2O3 facilitate mass transfer and intermediate enrichment. The synergisticPd1 and Pdc active sites enhance the adsorption and activation of CO2 and H2O. The localized surface plasmon resonance of Pd clusters induces a photothermal effect, further accelerating the reaction kinetics.","src":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/03\/Pd-single-atoms-Pd1-and-clusters-Pdc.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/03\/Pd-single-atoms-Pd1-and-clusters-Pdc.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/03\/Pd-single-atoms-Pd1-and-clusters-Pdc.jpg?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/03\/Pd-single-atoms-Pd1-and-clusters-Pdc.jpg?resize=700%2C400&ssl=1 2x"},"classes":[]},{"id":364,"url":"https:\/\/scienceblog.com\/sciencechina\/2026\/02\/13\/singapores-first-shipwreck-carried-a-record-haul-of-chinas-rarest-porcelain\/","url_meta":{"origin":367,"position":4},"title":"Singapore’s First Shipwreck Carried a Record Haul of China’s Rarest Porcelain","author":"SciChi","date":"February 13, 2026","format":false,"excerpt":"The ship itself is gone. Teredo worms saw to that \u2014 centuries of boring through waterlogged timber until every plank, every rib, every trace of the hull dissolved into the seabed at the eastern mouth of the Singapore Strait. What the worms couldn't eat was the cargo. And what a\u2026","rel":"","context":"In "Society"","block_context":{"text":"Society","link":"https:\/\/scienceblog.com\/sciencechina\/category\/society\/"},"img":{"alt_text":"YUAN DYNASTY BLUE-AND-WHITE PORCELAIN BOWL FRAGMENT PAINTED WITH A DRAGON INSIDE, 14TH CENTURY.","src":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/02\/porcelain.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/02\/porcelain.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/02\/porcelain.jpg?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2026\/02\/porcelain.jpg?resize=700%2C400&ssl=1 2x"},"classes":[]},{"id":265,"url":"https:\/\/scienceblog.com\/sciencechina\/2025\/09\/24\/ai-safety-net-blocks-toxic-chemical-recipes-while-preserving-scientific-progress\/","url_meta":{"origin":367,"position":5},"title":"AI Safety Net Blocks Toxic Chemical Recipes While Preserving Scientific Progress","author":"SciChi","date":"September 24, 2025","format":false,"excerpt":"Scientists have created a digital gatekeeper that could prevent artificial intelligence from accidentally becoming a how-to manual for making dangerous chemicals, while still allowing legitimate research to flourish. The system, called SciGuard, acts like a sophisticated bouncer for AI models used in chemistry labs worldwide. When someone asks an AI\u2026","rel":"","context":"In "Society"","block_context":{"text":"Society","link":"https:\/\/scienceblog.com\/sciencechina\/category\/society\/"},"img":{"alt_text":"Overview of AI risks and SciGuard framework.","src":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/09\/ai-safeguards.jpg?resize=350%2C200&ssl=1","width":350,"height":200,"srcset":"https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/09\/ai-safeguards.jpg?resize=350%2C200&ssl=1 1x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/09\/ai-safeguards.jpg?resize=525%2C300&ssl=1 1.5x, https:\/\/i0.wp.com\/scienceblog.com\/sciencechina\/wp-content\/uploads\/sites\/16\/2025\/09\/ai-safeguards.jpg?resize=700%2C400&ssl=1 2x"},"classes":[]}],"_links":{"self":[{"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/posts\/367","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/users\/1299"}],"replies":[{"embeddable":true,"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/comments?post=367"}],"version-history":[{"count":2,"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/posts\/367\/revisions"}],"predecessor-version":[{"id":369,"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/posts\/367\/revisions\/369"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/media\/371"}],"wp:attachment":[{"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/media?parent=367"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/categories?post=367"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/scienceblog.com\/sciencechina\/wp-json\/wp\/v2\/tags?post=367"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}