Melbourne-founded Indice Semiconductor has closed a $US6 million ($A6.9 million) Series A funding round that will help accelerate the distribution of its power-saving technology.
Its Continuous Sigma encoding method has the potential to reduce power consumption while increasing performance in everything from wearables, to headphones, computers and smartphones, co-founder James Hamond explains.
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“Digital-to-analog and analog-to-digital encoding methods are found in just about all modern electronic devices, from wearables, audio equipment, space probes, phones and so on,” he says.
“Indice’s patent-pending Continuous Sigma encoding method has the potential to reduce the device’s power consumption while increasing performance. For the end user this could mean headphones with more effective noise cancelling and crisper audio than ever before.
“Our advanced algorithms are game changers in other areas as well, including enabling the world’s smallest solar inverter – which is why we’ve entered Google’s Little Box Challenge.
“Eventually Indice hopes this will lead to more powerful and efficient electric motor control and car chargers, helping boost performance and consumer uptake of electronic vehicles.”
The Series A round was led by Allen Alley, founder of semiconductor company Pixelworks, who will join the startup, and Australian venture capital firm Rampersand. Hamond says a number of private individuals who make up the “old silicon guard” also invested. He declined to name those investors.
Founded in Melbourne in 2008, Indice Semiconductor has since relocated to Oregon in the United States. It’s sold one million of its Continuous Sigma powered chips to date, and plans to use the investment to scale up, targeting the Asian market.
So how was a startup that had received just $2.5 million in funding before this round able to create a solution that other big semiconductor makers couldn’t? Co-founder Aaron Brown says it has a lot to do with the fact the startup was bootstrapped.
“It’s quite amazing, but the large companies and other semiconductor manufacturers were using mathematics algorithms that are incredibly old, from the ‘30s, ‘40s and ‘60s, and their refining process relied on sheer brute force,” Brown says.
“We didn’t have the luxury of a large silicon foundry or design foundry, so we had to forge ahead to find a really elegant solution that could be applied to almost any semiconductor.”
But it was not an easy path, Hammond adds.
“Aaron and I like to joke if we knew how hard it was going to be we probably wouldn’t have started,” he says with a laugh.