M1076
The M1076 is the second product in Mythic's M1000 series of Analog Matrix Processors (Mythic AMP), designed for high-performance AI inference at the edge.
Publisher review
The M1076 is the second product in Mythic's M1000 series of Analog Matrix Processors (Mythic AMP), designed for high-performance AI inference at the edge. It targets applications with strict power, form factor, and thermal constraints, such as smart city surveillance, industrial machine vision, enterprise analytics, and consumer devices. The chip delivers up to 25 TOPS of AI compute in a 3–4W power envelope (typical for complex models), claiming 10× lower power consumption than comparable digital solutions like desktop GPUs. It is available as a standalone FCBGA chip (19mm × 15.5mm), an ultra-compact PCIe M.2 card (M-Key or A+E Key), or a larger PCIe card that can scale to 16 AMPs for up to 400 TOPS at 75W.
The M1076 integrates 76 Mythic AMP tiles, each containing a Mythic Analog Compute Engine (ACE) that stores DNN weight parameters in non-volatile flash memory and performs matrix multiplication directly inside the memory arrays. This eliminates the need for external DRAM, reducing both power consumption and data-movement latency. Each tile also includes a 32-bit RISC-V nano-processor (customized via Codasip L30), a 16-bit SIMD vector processor, dedicated SRAM, and a network-on-chip router. The chip supports INT4 and INT8 operations, stores up to 80 million weight parameters, and can execute multiple complex DNNs concurrently—including pre-qualified models for classification (ResNet-50), object detection (YOLOv3), human pose estimation (OpenPose Body25), depth estimation, and image segmentation. It uses a 4-lane PCIe 2.1 interface with up to 2 GB/s bandwidth.
Mythic's analog approach competes directly with digital edge AI processors from NVIDIA (Jetson series), Intel (Movidius), and Qualcomm (RB5 platform). The M1076's key differentiator is its analog compute-in-memory architecture, which claims to deliver GPU-class performance at a fraction of the power—25 TOPS at 3–4W versus a desktop GPU requiring 75–150W for similar throughput. However, the M1076 is not a general-purpose processor; it is optimized specifically for DNN inference and requires a proprietary software workflow (Mythic AI Workflow) to quantize, compile, and deploy models from PyTorch, TensorFlow, Caffe, or ONNX. The chip also supports deterministic execution, which is valuable for safety-critical or latency-sensitive applications.
Honest trade-offs: The M1076's analog nature introduces potential precision limitations—it supports only INT4/INT8, not FP16 or FP32, which may reduce accuracy for some models unless retrained. The reliance on a proprietary software toolchain means developers cannot simply drop in any DNN; they must go through Mythic's optimization and compilation pipeline. Additionally, the chip is not yet widely available on the retail market; as of the sources, it remains a niche product for early-access partners and evaluation kits. Finally, the 3–4W power figure is for 'typical complex models'—peak power may be higher, and the 25 TOPS rating assumes INT4 operations, with lower throughput for INT8.
How it works
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Analog compute-in-memory
Stores DNN weights in flash memory and performs matrix multiplication inside the memory arrays, eliminating external DRAM and reducing data movement.
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25 TOPS at 3–4W
Delivers up to 25 TOPS of AI compute in a 3–4W power envelope when running typical complex models, claiming 10× lower power than digital solutions.
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76 AMP tiles
Integrates 76 Analog Matrix Processor tiles, each with a Mythic ACE, a RISC-V nano-processor, a SIMD vector engine, SRAM, and a network-on-chip router.
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80M weight capacity
Stores up to 80 million DNN weight parameters entirely on-chip, enabling single-chip execution of large models without external memory.
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INT4/INT8 support
Supports INT4 and INT8 operations for efficient inference, with models quantized from FP32 via Mythic's software workflow.
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Multiple form factors
Available as a standalone BGA chip, M.2 M-key card, M.2 A+E key card, or PCIe card with up to 16 AMPs for scalable deployment.
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Pre-qualified DNN models
Includes pre-qualified models for classification, object detection, pose estimation, depth estimation, and segmentation, deployable via Mythic's AI workflow.
Strengths and trade-offs
Strengths
- Delivers up to 25 TOPS of AI compute at just 3–4W, achieving 10× lower power consumption than comparable digital solutions like desktop GPUs.
- Eliminates external DRAM by storing up to 80 million weight parameters on-chip, reducing system cost, power, and latency.
- Supports concurrent execution of multiple complex DNNs (e.g., ResNet-50, YOLOv3, OpenPose) entirely on a single chip.
- Available in multiple form factors (standalone chip, M.2 cards, PCIe card with up to 16 AMPs) for scalable deployment from edge to server.
Trade-offs
- Limited to INT4 and INT8 precision; no native FP16 or FP32 support may reduce accuracy for some models without retraining.
- Requires a proprietary software workflow (Mythic AI Workflow) for model quantization, compilation, and deployment, adding vendor lock-in.
- Not yet widely available on the retail market; as of the sources, it is targeted at early-access partners and evaluation kits.
- The 25 TOPS rating assumes INT4 operations; real-world throughput at INT8 is lower, and peak power may exceed the typical 3–4W figure.
Pricing context
Pricing is not explicitly stated in the sources; the M1076 is typically sold through direct engagement with enterprise partners and evaluation kit programs.
Getting started with M1076
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Request an evaluation kit
Contact Mythic directly through their website to request an M1076 evaluation kit. Provide details about your edge AI application, target power and performance requirements, and expected deployment scale to qualify for early-access partner programs.
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Install Mythic AI Workflow
Download and install the proprietary Mythic AI Workflow software suite on your development machine. This toolchain handles model quantization, compilation, and deployment for the M1076. Follow the provided installation guide for your operating system.
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Quantize your DNN model
Import your trained model from PyTorch, TensorFlow, Caffe, or ONNX into the Mythic AI Workflow. Use the tool to quantize the model weights from FP32 to INT4 or INT8 precision, as the M1076 only supports these integer formats. Verify accuracy with validation data.
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Compile and deploy the model
Compile the quantized model using the Mythic AI Workflow to generate a binary optimized for the M1076's analog compute-in-memory architecture. Connect the M1076 evaluation card via PCIe or M.2 interface, then flash the compiled model onto the chip using the provided deployment tools.
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Run inference and monitor power
Execute inference on your deployed model using the Mythic runtime API. Monitor real-time power consumption with the included profiling tools to confirm the 3–4W typical envelope. Adjust model complexity or batch size to optimize throughput for your edge application.
Frequently Asked Questions
What is the Mythic M1076 analog matrix processor?
The M1076 is Mythic's second-generation analog matrix processor for edge AI inference. It delivers up to 25 TOPS at 3–4W using analog compute-in-memory, storing DNN weights on-chip to eliminate external DRAM and reduce power consumption significantly.
How does the M1076 achieve 25 TOPS at only 3–4 watts?
The M1076 uses analog compute-in-memory, performing matrix multiplication directly inside flash memory arrays where DNN weights are stored. This eliminates external DRAM and reduces data movement, achieving up to 25 TOPS at 3–4W—about 10× lower power than comparable digital solutions.
What form factors is the M1076 available in?
The M1076 comes as a standalone FCBGA chip (19mm × 15.5mm), an ultra-compact PCIe M.2 card (M-Key or A+E Key), or a larger PCIe card that scales to 16 AMPs for up to 400 TOPS at 75W, enabling flexible edge deployment.
Which DNN models does the M1076 support?
The M1076 supports pre-qualified models including ResNet-50 for classification, YOLOv3 for object detection, OpenPose Body25 for pose estimation, plus depth estimation and image segmentation. It can run multiple complex DNNs concurrently on a single chip.
What are the main trade-offs of the M1076?
The M1076 supports only INT4 and INT8 precision, not FP16 or FP32, which may reduce accuracy for some models. It requires a proprietary software workflow for model quantization and compilation, and it is not yet widely available on the retail market.
How does the M1076 compare to NVIDIA Jetson or Intel Movidius?
The M1076 claims 10× lower power than desktop GPUs for similar throughput, at 25 TOPS in 3–4W versus 75–150W. However, it is optimized solely for DNN inference, lacks general-purpose compute, and requires Mythic's proprietary software toolchain for deployment.
Alternatives
How M1076 compares
Direct head-to-head against 3 competitors. Picked by 7wData.
M1076
- Pricing
- Pricing is not explicitly stated in the sources; the M1076 is typically sold through direct engagement with enterprise partners and evaluation kit programs.
- Target
- The M1076 is the second product in Mythic's M1000 series of Analog Matrix Processors (Mythic AMP), designed for high-performance AI inference at the edge.
- Strength
- Delivers up to 25 TOPS of AI compute at just 3–4W, achieving 10× lower power consumption than comparable digital solutions like desktop GPUs.
- Watch for
- Limited to INT4 and INT8 precision; no native FP16 or FP32 support may reduce accuracy for some models without retraining.
Iron Bull Dump Trailers
- Pricing
- $8,000-$15,000 for heavy-duty models
- Target
- Commercial hauling, construction debris
- Deployment
- Tandem/triple axle
- Strength
- Scissor-lift hydraulic system
- Watch for
- Higher weight reduces fuel efficiency
Big Tex Dump Trailers
- Pricing
- $3,000-$7,000 for compact models
- Target
- Landscapers, small businesses
- Deployment
- Single/tandem axle
- Strength
- Affordable entry pricing
- Watch for
- Lower payload capacity (1,500-2,000 lbs)
PJ Trailers Dump Models
- Pricing
- $6,000-$12,000 mid-range
- Target
- Farm equipment, long-term use
- Deployment
- Welded I-beam frames
- Strength
- Custom side extensions
- Watch for
- Limited tilt options
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Sources
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