What is ComputeRAM?
ComputeRAM is a Edge AI Accelerator IP core from Synthara listed on Semi IP Hub.
Vendor:
Synthara
Category:
Edge AI Accelerator
ComputeRAM
ComputeRAM is an SRAM macro with integrated compute capability.
Overview
ComputeRAM is an SRAM macro with integrated compute capability. It is a semiconductor IP product designed to enable microcontroller-based systems to run AI, DSP, and other linear algebra routines up to 130x faster and 150x more energy efficiently.
ComputeRAM enables licensees to seamlessly integrate in-memory computing capabilities into existing chip designs. A ComputeRAM macro shares the same memory interface as conventional SRAM and is compatible with any microcontroller-based SoC (Arm, RISC-V, x86, or otherwise).
ComputeRAM’s SDK allows programmers to develop and port new and existing libraries, such as PyTorch and Tensorflow, to a ComputeRAM-enabled system. When running linear algebra intensive applications, such as neural networks or digital signal processing routines, this results in dramatic performance gains. The SDK also includes libraries of ComputeRAM-optimised neural network building blocks and models that can be used for rapid development and deployment.
As an example, when a matrix-vector product is implemented on an Arm Cortex-M0 that uses ComputeRAM instead of conventional SRAM, it results in improvement of up to 130x in latency and 150x in energy-efficiency respectively.
ComputeRAM enables licensees to seamlessly integrate in-memory computing capabilities into existing chip designs. A ComputeRAM macro shares the same memory interface as conventional SRAM and is compatible with any microcontroller-based SoC (Arm, RISC-V, x86, or otherwise).
ComputeRAM’s SDK allows programmers to develop and port new and existing libraries, such as PyTorch and Tensorflow, to a ComputeRAM-enabled system. When running linear algebra intensive applications, such as neural networks or digital signal processing routines, this results in dramatic performance gains. The SDK also includes libraries of ComputeRAM-optimised neural network building blocks and models that can be used for rapid development and deployment.
As an example, when a matrix-vector product is implemented on an Arm Cortex-M0 that uses ComputeRAM instead of conventional SRAM, it results in improvement of up to 130x in latency and 150x in energy-efficiency respectively.
Key features
- Available as a 18 kB macro in GlobalFoundries 22FDX process; - Memory Compiler and FinFET variants under development
- Low power sleep mode with data retention
- Built using proven foundry SRAM bit cells, fully CMOS, strictly obeys foundry DFM/DRC rules
- Bit-accurate computation
- Matrix-vector product computation primitive
- Intermediate and full-precision modes
- Fixed point, signed and unsigned integer data types
- Programmable interrupt pin assert when computation completes
Block Diagram
ComputeRAM block diagram
Benefits
- Drop-in replacement to conventional memory with the added capability to compute on stored content
- Optimised for linear algebra workloads such as AI and DSP
- CPU agnostic and compatible with any ISA such as Arm, RISC-V, x86 or others
- Software development kit (SDK) ensures compatibility with standard development frameworks (Tensorflow Lite, PyTorch, ONNX) and is extensible to other BLAS-based libraries.
- SDK also includes libraries of optimised routines and kernels ready for deployment
Applications
- IoT
- Automotive
- Industrial use cases
- Wearables
- Embedded reality.
What’s Included?
- Testing resources: SystemVerilog and UVM testbenches, Verilog, C++ functional & SystemC models
- *.lib, *.lef, *.ndm, *.gds files for backend integration
- Software development kit (SDK)
- ComputeRAM emulation kit on Renode, support for other frameworks under development
- Datasheet, App Notes, User Guides
- Customisation services
Specifications
Identity
Part Number
ComputeRAM
Vendor
Synthara
Type
Silicon IP
Files
Note: some files may require an NDA depending on provider policy.
Provider
HQ:
Switzerland
Frequently asked questions about Edge AI Accelerator IP cores
How should engineers evaluate this Edge AI Accelerator?
Engineers should review the overview, key features, supported foundries and nodes, maturity, deliverables, and provider information before shortlisting this Edge AI Accelerator IP.
Can this semiconductor IP be compared with similar products?
Yes. Buyers can compare this product with similar semiconductor IP cores or IP families based on category, provider, process options, and structured technical specifications.