Synthara
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About
Synthara is a semiconductor technology provider based in Zurich that specializes in unleashing the potential of in-memory computing (IMC) for the embedded systems market. Its flagship product, ComputeRAM™, is designed to overcome the traditional von Neumann bottleneck where data must constantly travel between a memory unit and a processing unit. By performing computations directly within the memory itself, Synthara enables a dramatic shift in how embedded chips handle data-intensive tasks, particularly those involving artificial intelligence and complex sensor processing. The technology works by integrating seamlessly into standard embedded chip designs, effectively turning storage components into active processing units. This architecture results in a performance boost of up to 100x in terms of both speed and energy efficiency. Because the integration is handled at the silicon level without requiring massive overhauls of existing software stacks or adding significant silicon area, it provides a low-friction path for chipmakers to enhance their hardware's AI capabilities. The platform is built to be flexible and reliable, catering to a wide range of power-constrained environments. Synthara is ideally suited for manufacturers and designers in the wearables, robotics, and smart sensor industries. In wearables, the technology allows for sophisticated features like on-device voice assistants and health monitoring without sacrificing battery life. In robotics and drones, it provides the computational headroom needed for autonomous navigation and real-time decision-making. The company also works closely with automotive semiconductor designers to advance high-performance computing within vehicles, ensuring that modern cars can handle the influx of data from various sensors and safety systems. What distinguishes Synthara from other hardware acceleration solutions is its focus on "seamless integration" and its collaborative ecosystem. While many AI accelerators require proprietary tools and complex integration steps, Synthara’s approach allows device makers to unlock new processing opportunities without excessive added costs. Through partnerships with industry leaders like Synopsys and Siemens, the company has validated its technology for diverse use cases ranging from low-power smart sensors to large-scale AI accelerators for Large Language Models (LLMs).
Pros & Cons
Delivers a 100x boost in energy efficiency and speed for embedded chips.
Eliminates data bottlenecks by performing computation directly within memory units.
Integrates into existing designs without requiring additional silicon or software costs.
Supported by a strong ecosystem of partners including Synopsys, Siemens, and Intel.
Versatile enough to be used in everything from hearing aids to automotive semiconductors.
Requires hardware-level integration which is not suitable for software-only developers.
Pricing is not publicly listed and requires direct contact for custom quotes.
Use Cases
Wearable device manufacturers can use ComputeRAM™ to add advanced voice assistants to hearing aids while maintaining long battery life.
Robotics engineers can integrate the technology into drones to enable faster real-time autonomous decision-making and sensing.
Automotive semiconductor designers can partner with Synthara to create high-performance, energy-efficient chips for next-generation vehicle systems.
Smart sensor developers can deploy programmable sensors that are adaptable to various industrial use cases without excessive power draw.
AI hardware researchers can utilize ComputeRAM™ as a case study for accelerating Large Language Models (LLMs) in edge environments.
Platform
Features
• flexible hardware architecture
• programmable smart sensors
• embedded ai acceleration
• seamless chip integration
• energy efficiency optimization
• 100x speed improvement
• in-memory computing (imc)
• computeram™ technology
FAQs
What is ComputeRAM™?
ComputeRAM™ is Synthara's core technology that enables in-memory computing (IMC) for embedded chips. It eliminates the bottleneck between memory and compute units by performing calculations directly within the memory.
How much of a performance boost can I expect?
The technology delivers a 100x boost to both processing speed and energy efficiency. This is achieved by reducing the energy and time lost when moving data between storage and processors.
Does this require a complete software overhaul?
No, the technology is designed for seamless integration. It allows chip and device makers to unlock new capabilities without added software costs or significant changes to their existing silicon architecture.
Which industries can benefit from Synthara's technology?
The primary applications include wearables (hearing aids, fitness watches), robotics (drones, home automation), and programmable smart sensors. It is also used in automotive semiconductor design.
Pricing Plans
Enterprise / Custom
Unknown Price• ComputeRAM™ integration
• 100x boost to speed and efficiency
• Seamless silicon integration
• Support for wearables and robotics
• Automotive semiconductor design
• Access to IMC ecosystem
• Technical support and documentation
Job Opportunities
Senior Compiler Engineer
Boost speed and energy efficiency by 100x for embedded chips using in-memory computing technology that eliminates data bottlenecks in AI-driven applications.
Experience Requirements:
5+ years building low-level software or compilers
Strong C++ and Python
Hands-on experience with embedded systems and compiler design
Solid systems understanding: memory and concurrency fundamentals
Evidence of performance work (profiling, tracing, optimization) on embedded or accelerator targets
Other Requirements:
Comfortable reading hardware datasheets and working at the HW/SW boundary
Clear writing, good documentation habits, and a collaborative approach
Responsibilities:
Design and implement embedded software libraries and low-level runtime
Develop and maintain the compiler path (MLIR/LLVM passes, code generation, kernels)
Develop and refine a benchmarking and profiling framework
Strengthen build, test, and CI so releases are predictable
Collaborate with hardware, architecture, and customer-facing teams
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Lead Software Engineer
Boost speed and energy efficiency by 100x for embedded chips using in-memory computing technology that eliminates data bottlenecks in AI-driven applications.
Experience Requirements:
7+ years building low-level software or compilers
Strong C++ and Python
A track record of shipping complete projects end-to-end
Experience with roadmaps and reviews
Hands-on with MLIR/LLVM passes, code generation, graph compilers, or high-performance DSP libraries
Other Requirements:
Ability to lead independently and produce complete development plans (Jira, MS Project)
Ability to deal with hardware-specific language and define clean APIs
Responsibilities:
Own software development from a management perspective and distribute workload
Ensure correct exchange of information between hardware and software teams
Deliver clean programming models, drivers, kernels, and examples
Define compiler development priorities and plan functionalities
Grow the team: hire, mentor, and set ownership boundaries
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Senior Engineer – Backend (Physical Design & Timing)
Boost speed and energy efficiency by 100x for embedded chips using in-memory computing technology that eliminates data bottlenecks in AI-driven applications.
Experience Requirements:
5+ years in ASIC physical design with a strong emphasis on timing closure and sign-off
Familiarity with STA tools and flows
Hands-on experience working with synthesis and place-and-route
Scripting ability to automate timing analysis and reporting
Other Requirements:
A collaborative approach with front-end, backend, and verification teams
Responsibilities:
Build, refine, and maintain timing constraints for blocks and top-level designs
Run STA across all relevant corners and modes to close setup and hold
Guide clock-tree strategy and debugging (skew, jitter, latency)
Lead timing and functional ECOs and track impacts
Automate timing checks, report generation, and regressions using scripting
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