Epiq Solutions is excited to announce the Sidekiq™ NVM2 - the latest addition to Epiq's small form factor software-defined radio (SDR) portfolio. Packaged in the compact 3042 M.2 form factor, the NVM2 integrates the Analog Devices ADRV9004 with the AMD Artix 7™ FPGA to provide a high-performance low SWaP SDR ready for deployment in a range of host platforms. Sidekiq™ NVM2 is set to enable the next generation of applications for RF spectrum monitoring, small form factor payloads, counter unmanned aerial systems (cUAS), assured precision navigation and timing (APNT), and signals intelligence.

If troubleshooting is an art, troubleshooting a complex embedded device like a Software Defined Radio (SDR) is doubly challenging. 

As an SDR engineer, I know what it’s like to design a product you and your team are deservedly proud of… only to get an email the next morning from the customer support team asking for your help resolving an issue. 

When this happens, I have three options:

Today, we’re excited to announce a new partnership with DeepSig that will allow us to deploy artificial intelligence and machine learning (AI/ML) applications directly onto our software-defined radios (SDRs), making integration easier for our customers.

Epiq has spent the last 14 years focused on bringing low-SWaP software-defined radio platforms to the market in support of dozens of use cases for 400+ customers all over the world.  We started with the core idea that if we can develop state-of-the-art flexible RF hardware, do it in standards-compliant form factors from M.2 to 3U VPX, and wrap it all with a well-defined software API, the sky’s the limit for the products we can enable for our customers.

The world of AI and ML is evolving at a rapid pace. While much of the fanfare is focused on analyzing the surprisingly human-like output of ChatGPT, the usage of AI/ML in defense applications is also getting significant attention.  This is especially true for use cases where there is an ever-increasing amount of digital data to process in order to understand the RF spectrum and wireless threat landscape. In these applications, AI/ML techniques can bring a unique perspective to provide insights where classical processing may fall short for latency, data throughput, or processing SWaP-C.